esa-sceva/satcom-chunk-collection huggingface dataset:
# Impact of Putting on Traffic Safety: A Synthesis of Research Findings
## 3 Results and Discussion
### Simulation Studies  
As shown in Figure 4, there is a consensus among the simulation-based research on the impact of rutting on traffic safety [25; 26; 27; 28; 29; 30; 32; 33; 35; 36; 37; 38; 39; 48; 50; 51]. Notably, simulation-based studies utilized the flexibility of simulation models to investigate the impact of different factors on the effect of rutting, including environmental conditions, pavement surface conditions, driving speed, and driving paths.  
Several studies have focused on the impact of rutting on steering stability, which directly affects drivers' steering control. [PERSON] et al. [30] found that rutting causes vertical displacement of the vehicle, leading to a loss of steering control. This effect is more pronounced in vehicles without steering assist systems. [PERSON] et al. [32] examined the influence of rutting side angle and average width on steering control. They found that while rutting width does not significantly affect steering stability, rutting depth and side angle have substantial impacts on the steering control system. [PERSON] et al. [33] concluded that while the load transfer ratio (LTR) does not affect safety, the roll angle fluctuates beyond safety limits when crossing ruts. [PERSON] et al. [35] stated that rutting depth impacts the roll angle but has a minimal effect on lateral acceleration on dry pavement. They emphasized the need for greater attention to wet pavement conditions, where rutting has a significantly higher impact on safety.
## 3 Proposed System Model
### Considered Architecture  
The coverage of the cellular communication network is split into a series of tracking areas, each containing a gNodeB serving the region. Each gNodeB is identified by its Cell Identifier (CID) and Tracking Area Code (TAC). Figure 1 shows a scenario in which some legitimate BS provide 5G coverage for an area containing a segment of a motorway along which platoons of vehicles regularly travel. The physical location of the platoon leader is given by (\(X_{pos}\), \(Y_{pos}\)) which is used to calculate the distance between each BS and the leader.  
The moving UE elements here are vehicles that travel approximately 80 km/h. The platoon leader regularly exchanges messages every few milliseconds with the application server in a 5G core [35]. This presents very challenging latency requirements that are satisfied by the Ultra-Reliable Low-Latency Communications (URLLC) mode as defined in the 5G standards. Two modes of communication were used: V2V [36] between cars in the platoon and V2N between the platoon leader and BS. Of these, V2V is satisfied by URLLC, but V2N can be compromised by the presence of an RBS. To develop techniques to protect against such attacks, we require realistic BS transmission data--this is the focus of this study.
# Privacy-Preserving Electricity Trading for Connected Microgrids
## 5 System Description
### System Operation
#### 5.4.3 Transmission and Management of Energy Requests  
When a smart meter detects its household needs more energy than it produces:  
1. It generates and transmits as many energy requests (Section 5.3, Procedure 5) as required in order to meet its consumption needs.
2. Upon receiving a request, if there are available local offers, the substation pairs the request with an available offer. * During the pairing process, the requesting meter makes an e-coin payment for the acquired piece of energy. * After the pairing process has been completed, the requesting smart meter can take the acquired energy from the grid during the established time.  
3. If there are no local offers in the microgrid, the substation generates an offer by itself and pairs it. * In such case, the energy is supplied from the global grid. 4. When the supply time established as a result of a pairing is about to elapse: * If the consuming meter expects to continue with that consumption of energy, it has to send a new request message. * Otherwise, when the supply time elapses, the meter has to stop consuming that energy from the grid.
## 2 Methodology
### Principle of Extreme Gradient Boosting Tree  
[PERSON] designed the extreme gradient boosting tree (XGBoost), and the core of the XGBoost algorithm is the integration of multiple weak learners to build a single strong learner by progressively optimizing the loss function [40,41]. The input samples of each decision tree and its predecessor tree are trained to correlate with the prediction results, and finally, the prediction results of all the decision trees are accumulated as the final prediction results. Assuming that the model itself consists of N decision trees, the training dataset samples are \(D=\left\{(x_{i},y_{i})\right\}(i=1,2,\ldots,n,x_{j}\in R^{l},y_{j}\in R)\), which contain n samples and J features, where \(x_{i}=x_{1},x_{2},\ldots\ldots x_{n}\) denotes the input feature values, \(y_{i}\) denotes the labels, and \(\hat{y}_{i}\) is the predicted value for the ith sample, and the model objective function is Equation (6):  
\[obj(t)=\sum_{i=1}^{n}L(y_{i},\hat{y}^{(t)})+\Omega\sum_{j=1}^{t}\left(f_{j} \right), \tag{6}\]  
The first term of the objective function represents the loss function of the predicted value of the entire strong learner with respect to the target value. The second term represents the complexity of the \(j\) weak learners in the strong learner. The optimization objective of the model is to minimize the loss function until the predefined stopping condition is reached. This objective is expressed in Equation (7):  
\[argmin_{c}\sum_{i=1}^{N}L(y_{i},\hat{y}_{i}^{(t)})+\Omega\sum_{j=1}^{t}\left(f _{j}\right), \tag{7}\]  
If the previous \(t-1\) regression tree has been trained and the objective function has been added t times, then Equation (8) is obtained:  
\[obj(t)=\sum_{i=1}^{n}L(y_{i},\hat{y}^{(t-1)}+f_{t}(x_{i}))+\Omega\sum_{j=1}^{ t}\left(f_{j}\right)+\Omega(f_{t}), \tag{8}\]  
Based on the second-order derivative of [PERSON]'s formula Equation (9), the objective function can be transformed into Equation (10):  
\[f(x)\approx f(x_{0})+f^{\prime}(x_{0})(x-x_{0})+\frac{1}{2}f^{\prime\prime}( x_{0})(x-x_{0})^{2} \tag{9}\]  
\[obj(t)\approx\sum_{i=1}^{n}\left[L(y_{i},\hat{y}^{(t-1)})+g_{i}f_{t}(x_{i})+ \frac{1}{2}h_{i}f_{t}^{2}(x_{i})\right]+\Omega\sum_{j=1}^{t}\left(f_{j}\right) +\Omega(f_{t}) \tag{10}\]where \(g_{i}\) and \(h_{i}\) are the first-order derivative Equation (11) and the second-order derivative Equation (12) of the loss function, respectively:  
\[g_{i}=\frac{\partial L(y_{i},\hat{y}^{(t-1)})}{\partial\hat{y}^{(t-1)}} \tag{11}\]  
\[h_{i}=\frac{\partial^{2}L(y_{i},\hat{y}^{(t-1)})}{\partial\hat{y}^{(t-1)}} \tag{12}\]  
The complexity of each weak learner of XGBoost is determined by two factors: \(\gamma T\) and \(\lambda\left\|w\right\|^{2}\), as indicated in Equation (13).
## Materials and Methods
### SEAMCAT Simulation
#### 2.4.1 Simulation of Interference Model  
As shown in Figure 1, the interference scenario where the effectiveness of FSS-ES might be seriously affected by a practical 5G deployment is evaluated to analyse and determine the effect of interference from 5G-BS on FSS-ES. The 5G-BS is assumed to be deployed symmetrically around the circle's circumference through a radius matching protection distance via the FSS-ES centre distance. The number of 5G-BS is computed by using Equation (1) [16].  
\[ND_{BS}=\frac{2\pi d_{p}}{0.8} \tag{1}\]  
where \(ND_{BS}\) refers to the number of deployed BSs. The protection distance is denoted by \(d_{p}\) (km) and concentrated on two types of interference: co-channel and adjacent channel interferences. In other words, each sector has one specific UE. The aggregate interference of all 5G-BSs is then computed by using Equation (2). However, the received signal power DL from the 5G-BS into UE is iRSS.  
\[I_{aggregated}=10 log\sum_{n=1}^{n=ND_{BS}}10^{iRSS_{n}/10} \tag{2}\]
# WLAN Aware Cognitive Medium Access Control Protocol for IoT Applications
## 7 Conclusions  
In this paper, we consider a co-existence scenario in which low-powered IEEE 802.15.4 IoT devices are subjected to interference caused by high-powered IEEE 802.11 devices. We propose a WLAN Aware Cognitive Medium Access Control (WAC-MAC) protocol that is adopted by the IoT users to reduce the interference of co-existing WLAN users. The proposed WAC-MAC alters the superframe structure of the IEEE 802.15.4 to introduce the sensing phase based on energy detection and incorporates adaptive wake-up scheduling in case the medium is busy. Moreover, an adaptive backoff scheme based on the battery of the sensor node is also included to reduce collisions and improve network lifetime. Results show that the proposed WAC-MAC protocol increases the number of received packets and reduces the energy consumption of IoT nodes.  
**Author Contributions:** This article was prepared through collective efforts of all the authors. [PERSON], [PERSON], [PERSON] and [PERSON] contributed to the algorithm design and paper writing. [PERSON] and [PERSON] performed the simulation studies. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
Figure 16: Network lifetime.
# Highly Efficient GaN Doherty Power Amplifier for N78 Sub-6 GHz Band 5G Applications
## 3 Circuit Design of RF DPA
### Load Modulation in DPAs  
Load modulation in DPAs enhances efficiency and linearity by adjusting the carrier amplifier's load impedance, optimizing its efficiency and reducing heat generation. It also aligns the carrier amplifier's load with the peaking amplifier's output power, reducing distortion and enhancing signal quality. When combined with techniques like bias modulation and envelope tracking, it further improves DPA performance. Both amplifiers employ an output combiner with quarter-wavelength transmission lines, providing a 90-degree phase shift and contributing to load modulation. This modulation adjusts the impedance presented to the PA's output, varying with the current from the other device, as seen in Figure 6[29].  
When the input power is low (in back-off conditions), only the carrier PA is active and the impedance presented to the peaking PA (\(Z_{P}\)) will be \(\infty\). The impedance at the combination point \(\left(Z_{\zeta}\right)\) is first transformed to 25 ohms using the 35.35-ohm impedance transformer as per Equation (9), and the impedance presented to the carrier PA \(\left(Z_{\ C}\right)\) is 100 ohms as per Equation (10). When the input power increases, the peaking PA becomes active. At this time, the output impedance presented to the carrier PA and the peaking PA are both 50 ohms as per Equation (10). In both conditions, considering the 35.35-ohm impedance transformer performance, the load impedances of the main and peaking amplifiers change based on given information and the value of impedance at the combination  
Figure 5: Unequal Wilkinson power divider. (**a**) The layout (dimension in mm). (**b**) EM simulation result versus frequency.  
Figure 6: The schematic diagram of a DPA with the principle of load modulation for adjustment output impedance of the amplifier.  
point \(\left(Z_{\mathcal{C}}\right)\) lead to 25 \(\Omega\). This impedance would be transformed to the output impedance (\(R_{L}\)= 50 \(\Omega\)) using a 35.35 \(\Omega\) impedance transformer as depicted in Figure 6.  
\[Z_{\mathcal{C}}=\left(Z_{T}\right)^{2}/R_{L}, \tag{9}\]  
\[Z_{\mathcal{C}}=\left(Z_{I}\right)^{2}/[R_{L}(1+\left(I_{P}/I_{\mathcal{C}} \right))], \tag{10}\]
## 3 Results and Discussion
### SCCO\({}_{2}\) Regeneration
#### 3.2.2 Mass Transfer Limitations: Effect of Time, Flow, and Pressure
Figure 8 shows the effect of pressure on regeneration at 200 \({}^{\circ}\)C during 15 min with a SCCO\({}_{2}\) flow of 3 cm\({}^{3}\cdot\)min\({}^{-1}\). Figure 8: Effect of pressure on RE for SCCO\({}_{2}\) regeneration at 200 \({}^{\circ}\)C with a SCCO\({}_{2}\) flow of 3 cm\({}^{3}\cdot\)min\({}^{-1}\) during 15 min. Figure 7: Effect of the SCCO\({}_{2}\) flow on RE for SCCO\({}_{2}\) regeneration at 100 bar and 200 \({}^{\circ}\)C during 15 min. As SCCO\({}_{2}\) was more pressurized, the regenerated ACF recovered a greater percentage of its original adsorption capacity. The process was limited by solubility under the conditions used in this specific study. The solubility of phenol in SCCO\({}_{2}\) increased as the fluid was pressurized and, as a consequence, the extraction pathway was also improved [10; 13; 17]. This last study reveals that pressure modification may be the most interesting way to optimize SCCO\({}_{2}\) regeneration. At 200 \({}^{\circ}\)C and a pressure high enough the ACFs are effectively regenerated. Although it is still an energy-demanding temperature, it means a noticeable reduction of the energy requirements in comparison with the classical thermal regeneration carried out at temperatures above 700 \({}^{\circ}\)C. Furthermore, it neither requires an excessive waste of regenerating fluid. The necessity for devices made with special materials able to support high pressures and the energy waste needed to work at pressures are their major drawbacks. In this way, the compression of the fluid must be controlled: an optimal pressure will exist above which the increase in solubility will be negligible [22].
# A Review of Adsorbents for Heavy Metal Decontamination: Growing Approach to Wastewater Treatment
## 5 Adsorption Isotherm  
The adsorption isotherm model is a valuable method for determining the theoretical optimal adsorption power as well as the potential interactions between adsorbents and adsorbate [88]. Sorption isotherms are mathematical models that illustrate the allocation of metals in between adsorbate and adsorbent. The distribution of metals in between adsorbate and adsorbent depends upon the nature of the adsorbent whether it is homogeneous or heterogeneous, the type of exposure, and the bonding between adsorbent and adsorbate. In this study, models of adsorption isotherm of adsorbents were studied by utilising Langmuir and Freundlich. The Langmuir isotherm assumes that all binding sites for the adsorbate have an equal affinity, resulting in the creation of a monolayer of adsorbed molecules [89]. The Freundlich isotherm, on the other hand, primarily defines adsorption into heterogeneous surfaces with adsorption sites of different affinities which were defined as follows [90]. Table 1 shows the linear and non-linear expressions of the Langmuir and Freundlich isotherms.
## 2 Wideband Antenna Designing
### Results of Unit Elements
#### 2.2.1 \(\mathrm{S_{11}}\)  
Figure 5 represents the comparison between the measured and simulated S-parameter of the proposed ultra-wideband antenna. It can be seen from the figures that the antenna offers broadband ranging from 26.5 to 43.7 GHz with resonance frequencies of 30 GHz, 34, and 38 GHz. The proposed work covers the 5G band for millimeter wave application. The antenna offers a low value of return loss with a minimum \(-\)38 dB and maximum \(-\)15 dB.  
Figure 4: Fabricated prototype of wideband antenna.  
Figure 3: Parametric analysis of (**a**) the radius of the circular patch and (**b**) length of the lower stub.  
The results also show similarity between simulated and measured results, which makes the proposed work the best applicant for future 5G devices operating at broadband.
## 2 Materials and Methods
### Data and Variables  
Intra-abdominal pathologies requiring emergency treatment, such as acute appendicitis, pancreatitis, cholecystitis, cholangitis, intestinal obstruction, GI perforation, and gastrointestinal bleeding, were included in the definition of GI emergencies. The inclusion and exclusion Criteria were as follows: 18 years of age or older, the definite diagnosis made in the emergency department, being in the GI emergency disease group defined above, and the necessary clinical data being recorded completely. Intra-abdominal injuries due to trauma or acute abdomen due to gynecological causes were excluded from the study as non-GIS etiologies. Patients with missing serum albumin or complete blood count results (PNI could not be calculated) were also excluded. Additionally, patients with known or suspected acute lymphoproliferative diseases were excluded from the study because they could have affected PNI assessment. A total of 583 patients who were found to be eligible because of the specified criteria constituted the final sample.  
Demographic characteristics (age and sex), presenting complaints, and final diagnoses were recorded from the patient files. In addition, outcomes in the emergency department (discharge, hospitalization in the ward, transfer to the intensive care unit, or exitus) were obtained from the records. Serum albumin levels (g/dL) and total lymphocyte counts (/mm\({}^{3}\)) measured from blood samples taken on admission were noted.  
The PNI was calculated for each patient using these parameters. PNI was calculated as (PNI = 10 \(\times\) serum albumin [g/dL] + 0.005 \(\times\) lymphocyte count [/mm\({}^{3}\)]).  
Due to the retrospective design of the study and the lack of detailed fluid balance records, serum albumin values were used as recorded and were not adjusted for hydration status.
# Mobility- and Energy-Aware Cooperative Edge Offloading for Dependent Computation Tasks +
### Overview of Contributions and Structure of This Article  
Our main contribution of this article, which extends the conference paper [24], is the development and evaluation of the online Energy-Efficient Task Offloading (EETO) algorithm that minimizes the battery energy consumption in mobile end devices with dependent computation tasks. Towards the development of EETO, we explicitly model the dependencies of the individual computation tasks of an application request in a task dependency graph in Section 3.2. We incorporate the recently developed and validated deep-learning-based PECNet user trajectory prediction into our model in Section 3.5 to account for the sojourn times of a mobile UE that offloads tasks within the coverage areas of the various prospective presently nearby (but mobile) helper nodes that can assist by taking over computation tasks or relaying computation tasks to a stationary MEC server.  
We formulate the energy minimization problem in Section 4, considering the battery energy expenditures for task computation, task data transmission, and waiting for offloaded tasks to complete. In order to efficiently solve the energy minimization problem, which is a non-convex mixed integer nonlinear program, we conduct a transformation to a quadratically constrained quadratic programming (QCQP) problem in Section 5.1 and specify the EETO algorithm in Section 5.2. To the best of our knowledge, the EETO algorithm is the first task offloading algorithm in a device-enhanced MEC setting that accommodates dependent computation tasks and employs deep-learning-based trajectory prediction for both the mobile UE that offloads tasks as well as the mobile helper nodes. The performance evaluation in Section 6 indicates that EETO flexibly accommodates a wide range of scenarios and parameter settings and consistently achieves low battery energy consumption, whereas benchmarks typically perform well only for a specific scenario or narrow parameter range.
# River Water Quality Monitoring Using LoRa-Based IoT
## 1 Introduction  
Water quality control has been traditionally carried out by collecting some samples and taking them to a laboratory where tests would be performed and a conclusion drawn based on the collected samples [1]. This takes a lot of time and resources. While this traditional approach is appropriate for carrying out detailed and broad surveys over a long period of time, it is ideally not practical in day-to-day operations aimed at protecting rivers and even being proactive [1].  
This experience aims at creating and prototyping an efficient and effective river water monitoring system that employs modern technology. This prototype takes the benefits of LoRa technology such as large coverage area, low power requirement and not relying on a third party. Lastly, the prototype is designed to be rugged, lightweight, easy to assemble, and inexpensive.  
With this developed prototype, the following water quality parameters will be monitored:  
* pH--Indicates the acidity or alkalinity of a solution [2]. It does not represent the measure of the quantity of acids or bases, but the relationship between them. The pH varies between 0 and 14. High or low pH values can be indicative of pollution, with normal values being considered between 6.5 and 8.5.
* Conductivity--This is an important parameter due to the ease of detecting contamination levels when measuring water conductivity [3]. High conductivity means that the water contains a high level of contaminants, and the opposite means that drinking water is practically incapable of conducting electrical current. The unit of measurement for electrical conductivity is mS/cm and the normal value is up to 2 mS/cm.  
* Turbidity--Is a measure of water transparency [4]. Cloudy water is often caused by suspended particles such as silt, (seaweed, etc.), organic materials, etc. These particles soak up (like a towel) and scatter the light rays instead of letting them pass through the water, which is detrimental to water-based plants and animals. A high turbidity value indicates cloudier water, and a low value means clear water. Turbidity measurements are represented in Nephelometric Turbidity Units (NTU) and normal values are up to 5 NTU.
* Temperature--There is no ideal temperature for river water, but many water-based organisms are sensitive to high temperatures and the oxygen solubility is lower in warmer waters, thus limiting the supply of oxygen [5]. The unit for temperature is degrees Celsius (\({}^{\circ}\)C).  
This article begins by presenting the current state of IoT usage and other related works in Section 2. Section 3 resumes an investigation that was carried out about some communications technologies used in IoT and explains LoRa controlling/adjusting. In Section 4, the earlier developed model is explained in some detail, and in Section 5, results are presented. In Section 6, we write conclusions and some statements about future work.
# Fake Base Station Detection and Link Routing Defense +
## 3 Background and Primer  
In this section, we provide a brief overview of the telecommunication architecture and the connectivity setup process. In Section 3.1, we provide an overview of 5G network architecture, as shown in Figure 1. We describe the wireless communication channel setup of the radio resource control (RRC) between the user equipment and the base station in Section 3.2 and the digital non-access stratum (NAS) between the user equipment and core network via a base station in Section 3.3. We borrow the RRC and NAS terms from the 3 GPP 5G New Radio standardization and use them in our paper, and we show the protocol in Figure 2a; RRC is shaded in yellow while NAS is shaded in blue. Both the RRC-layer and NAS-layer protocols and interactions are for connectivity setup, i.e., once the NAS completes, the user equipment can use the connectivity service for remote access.  
Our scheme design and implementation are based on the standardized 5G protocol. Although our detection and identification can be applied to the earlier generations of telecommunication protocols, e.g., 2G-4G, we focus on the 5G NR protocol because 5G is the most recent and has the strongest security in its authentication and key agreement (AKA).  
Figure 1: 5G cellular network architecture with network entities, including user equipment, base station, and 5G core network.  
Figure 2: _Cont_.