esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
In this work, a comprehensive performance analysis of single and multi-hop Zigbee communication was performed based on the real-world indoor and outdoor testbeds and measurements where the XBee S2C module was used as the transceiver. Performance metrics, such as PDR, power consumption, link quality, latency, and throughput were analyzed with the variations in transmission power level, transmission distance, packet size, baud rates, deployment scenario, and data encryption. This work also drew out the trade-offs and performance limitations of these metrics, which would benefit both academia and the industry for designing and deploying Zigbee in both indoor and outdoor applications. The study shows that the transmission power level of 3 dBm is the most optimized one in terms of PDR and energy consumption, i.e., the node lifetime. Node life can reach up to 0.94 years with a battery of 5000 mAh, transmitting 30 bytes/packet in each minute. Moreover, 3 dBm transmissions show promising performances, also in link quality, maintaining \(-95\) and \(-82\) dBm for single and two-hop, respectively, in a compacted office environment with a transmission distance of 40 m. Even though multi-hop communication can increase the transmission radius significantly, it can also increase the latency up to  
Figure 14: Throughput vs. packet size at three different baud rates for (**a**) indoor and (**b**) outdoor deployment.  
517.43%. On top of it, AES 128-bit encryption can degrade the latency up to 153.52%. These are the trade-offs of multi-hop and encrypted communication that one must consider while designing the network. The analysis also presented the improvement of the throughput performance with higher baud rates, better LoS, less interference, and higher packet sizes.  
We should note that the experiment that was conducted in this study was based on the Digi XBee S2C module, and the performance may vary slightly with the change of transceiver modules.  
Conceptualization, K.F.H. and K.Y.; methodology, K.F.H. and K.Y.; software, K.F.H.; validation, K.F.H.; formal analysis, K.F.H. and A.A.; resources, A.A. and K.Y.; writing--original draft preparation, K.F.H.; writing--review and editing, K.F.H. and K.Y.; supervision, A.A. and K.Y.; project administration, A.A. and K.Y.; funding acquisition, K.Y. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
# Efficient Distributed Method for NLOS Cooperative Localization in WSNs
## 4 Solution to the Subproblem
### Source Nodes  
For source nodes, local Subproblem (22) has the equivalent simpler form  
\[\min h(\mathbf{z}_{i})+g(\mathbf{z}_{i}) \tag{31}\]  
the proximal mapping of function \(h(\mathbf{z}_{i})=\frac{1}{2}\text{dist}^{2}(\mathbf{H}_{i}\mathbf{z}_{i},\text{C}_{i})\) is  
\[\text{prox}_{\mu h}(\mathbf{v}_{i})=\mathbf{H}_{i}^{\dagger}\left[\mathbf{H}_{i}\mathbf{v}_{i} +\frac{\mu}{1+\mu}\left(\text{P}_{\text{C}_{i}}(\mathbf{H}_{i}\mathbf{v}_{i})-\mathbf{H}_ {i}\mathbf{v}_{i}\right)\right] \tag{32}\]  
where \(\mu=1/L_{g}\) is the step size, \(\mathbf{H}_{i}^{\dagger}\) is the pseudoinverse of \(\mathbf{H}_{i}\), and \(\text{P}_{\text{C}_{i}}(\mathbf{H}_{i}\mathbf{z}_{i})\) is the orthogonal projection of point \(\mathbf{H}_{i}\mathbf{z}_{i}\) onto the convex set \(\text{C}_{i}\), _i.e._,  
\[\text{P}_{\text{C}_{i}}(\mathbf{H}_{i}\mathbf{z}_{i})\in\arg\min\{\|\mathbf{x}-\mathbf{H}_{i} \mathbf{z}_{i}\|\ |\mathbf{x}\in\text{C}_{i}\} \tag{33}\]  
Hence, we can obtain the accelerated proximal algorithm for local Subproblems (31), as follows, for \(k\geq 1\):  
\[\begin{split}\mathbf{\omega}_{i}^{(k-1)}&=\xi_{i}^{(k- 1)}-\mu_{k}\
abla g(\xi_{i}^{(k-1)})\\ &=\xi_{i}^{(k-1)}-\mu_{k}c\left[\xi_{i}^{(k-1)}-(\mathbf{A}_{i}\mathbf{z} ^{t}-\tilde{\mathbf{\lambda}}_{i}^{t})\right]\\ \eta_{i}^{(k)}&=\frac{1}{1+\mu_{k}}\mathbf{\omega}_{i}^{( k-1)}+\frac{\mu_{k}}{1+\mu_{k}}\mathbf{H}_{i}^{\dagger}\text{P}_{\text{C}_{i}}(\mathbf{H}_ {i}\mathbf{\omega}_{i}^{(k-1)})\\ \xi_{i}^{(k)}&=\eta_{i}^{(k)}+\frac{k-1}{k+2}(\eta_{ i}^{(k)}-\eta_{i}^{(k-1)})\end{split} \tag{34}\]This iterative procedure stops when the stopping criterion is satisfied. \(\mu_{k}\) is the step size. Here, we used the fixed step size \(\mu_{k}=\mu=1/L_{g}\). We used the estimation results of the last loop as the initial point of this subupdate, i.e., \(\mathbf{\xi}_{i}^{(0)}=\mathbf{z}_{i}^{t}\). The subupdate outputs \(\mathbf{\tilde{x}}_{i}^{(k)}=z_{i}^{t+1}\) as the local estimation result.
# The Analysis of Service Convergence in an Optical Access Network +
###### Abstract  
In recent years, the increasing number of internet-connected devices has exceeded the capacity of fourth-generation (4G) cellular networks, leading to the development of fifth-generation (5G) technology, designed to offer higher speeds, greater bandwidth, and lower latency. In this context, this study evaluated Universal Filtered Multi-Carrier (UFMC) and Generalized Frequency Division Multiplexing (GFDM) techniques, implementing them in a radio-over-fiber (RoF) system and a Next-Generation Radio Access Network (NG-RAN) fronthaul link, and compared the results using communication quality metrics such as bit error rate (BER). Additionally, through signal generation and processing in Matlab, the performance of UFMC and LTE signals was analyzed, confirming that simultaneous transmission over an RoF channel allows for efficient signal separation in the frequency domain, with the UFMC giving power to LTE.  
5G; UFMC; GFDM; LTE; radio over fiber +
Footnote †: This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).
# Towards a Multi-Perspective Time of Flight Laser Ranging Device Based on Mirrors and Prisms
## 4 Discussion
Another study that can be used for comparison used a movable mirror to redirect a portion of VLP-16's FOV [16]. It obtained measurement errors that ranged from comparable to significantly worse than the reference measurements obtained by direct measurements in [28]. Five of the twelve provided error measurements where FOV was redirected by a mirror that exceeded the advertised measurement tolerance of the lidar, which was in itself twice the expected amount according to [28]. A large contributor to this discrepancy may have been the tolerances for mirror positioning, as we found measurement results for direct ranging and ranging through a clean mirror to be effectively indistinguishable from one another. The alternative explanation is that the mirror used in [16] was not clean in at least some parts of the experiments. In our tests, the use of prisms has proven to be more problematic than mirrors. When a prism is placed within the ranging device's dead zone, its influence is rather small, although still observable. This can be observed in Figure 10a, where the blue trace (prism 5 cm from the ranging device) trends towards increasingly negative measurement errors as the target moves away. Since the prism's first leg was in the ranging device's dead zone, this reflection likely did not contribute much. Therefore, internal reflections are the likely culprit. When the prism is positioned 10 cm from the ranging device or further in our setup, both of its legs rest within the ranging device's partially dead zone. This means that reflections off of both surfaces may be coupled straight back into the receiving photodiode. When the target is nearby, the signal's strength is strong enough to dominate the parasitic reflection; thus, the measurements are accurate. As the target moves further away, its influence slowly diminishes until eventually, the parasitic reflections take over entirely. When this happens, the measurements plateau, which can be clearly observed in Figure 10b. The discussed results were obtained with a clean prism with AR coated legs, which is the best-case scenario for redirecting FOV of a light ToF ranging device with a prism. With that in mind, it can be concluded that prisms are inappropriate for redirecting the FOV when the reflecting surfaces are beyond the ranging device's dead zone.
# Performance of Sensor Data Process Offloading on 5G-Enabled UAVs
## 2 UAV as UE
### UAV's Data Flows  
UAV Payload Communication  
Uplink usage is high in many robotics applications, particularly UAV applications. Many paradigms in the literature and industry, such as collaborative robots, centralized processing, and real-time calculations in the cloud, are constrained by communication delays and uplink throughput. It is vital to understand and analyze how data flows affect mission reliability and the communication boundaries that characterize such applications. As previously described, UAVs that are equipped with many modern sensors produce enough data to exceed the capabilities of any wireless communication system. Engineers must handle sensor data correctly to overcome such constraints and utilize the benefits of wireless cellular communication. Many studies research this topic, such as [10].  
During this study, a 5G-UAV is equipped with several sensors and utilized to assess the communication scheme's capabilities. The aim is to determine which processes can be offloaded to external resources, such as other robotic agents or the edge cloud. The selection of the sensors is inspired by the essential equipment that would enable a fully autonomous UAV. More specifically, the considered paradigm utilizes a 3D LIDAR sensor, an RGB depth color camera, an IMU sensor, and a GPS sensor. Such an autonomy package, together with the possibility of processing sensor data on the edge server and transmitting valuable information back to the platform, would suggest significant improvements in the operation of an autonomous UAV [18]. Section 2.2 presents a theoretical analysis of the 5G-UAV throughput capabilities in the 5G new radio (NR) physical layer. Figure 2 depicts the communication architecture where a 5G-enabled UAV is transmitting sensor data to a remote agent for further processing and storing, in this scenario, the edge cloud constitutes the remote agent.  
Figure 1: A 5G-UAV, control and non-payload communication, and payload communication.  
Figure 2: The 5G-UAV (UE) communication system architecture.
# Inclence of Chronic Pain following Oral Soft Tissue Biopsies: A Retrospective Study
###### Abstract  
Soft tissue oral biopsies (STOB) are commonly performed in the medical field to obtain tissue samples for diagnostic purposes. However, like all invasive procedures, these biopsies may carry risks, such as the development of chronic long-lasting neuropathic pain. This study aims to examine the occurrence of chronic pain following STOBs. A retrospective study was conducted on the medical records of 695 patients who underwent STOBs at the Maxillofacial Surgery Department of Hadassah Medical Center between the years 2013 and 2022. Demographic information, details of the procedure, histopathological diagnosis, and data related to post-procedure pain in the biopsy area were collected and analyzed. Out of the 695 patients, only 4 reports of chronic pain with mild intensity lasting more than three months following oral biopsy were documented, with incidence rate of 0.57%; all were females with a median age of 59 \(\pm\) 2.0 years. The biopsies were taken from various sites and the common diagnoses were lichen planus, venous malformation, and fibromas. In conclusion, chronic pain after oral soft-tissue biopsies is rare, generally mild, and typically does not require extra therapy. These insights can aid clinicians and patients in making informed decisions about these procedures.  
post-traumatic neuropathy; neuropathic pain; biopsy; soft-tissue biopsy; orofacial pain
# Effect of MXene Nanosheet Sticking on Supercapacitor Device Performance
## 5 Conclusions
### Prospect of MXene-based Supercapacistors  
The prospects of MXene-based supercapacitors are highly promising, offering several key advantages that make them attractive for energy storage applications:  
High specific capacitance: MXenes have demonstrated high specific capacitance, allowing for the storage of significant amounts of electrical charge per unit mass, making them well-suited for high-performance supercapacitors.  
Rapid charge/discharge rates: MXenes exhibit excellent charge/discharge kinetics, enabling rapid energy storage and release. This property is advantageous for applications requiring quick bursts of power, such as in regenerative braking systems or energy harvesting devices.  
Wide potential windows: MXenes have the potential for high operating voltages, which allows for a wide potential window. This characteristic can contribute to increased energy density and overall device performance.  
Good electrical conductivity: the metallic conductivity of many MXene compositions facilitates efficient charge transport within the material, contributing to low internal resistance and high power density in supercapacitor devices.  
Tunability and versatility: MXenes' tunable surface chemistry and compositional flexibility offer the potential for tailoring properties such as wettability, interlayer spacing, and surface functional groups, allowing for customized electrode designs to optimize supercapacitor performance.  
Excellent mechanical properties: some MXenes exhibit exceptional mechanical strength and flexibility, which can be leveraged for the development of mechanically robust and flexible supercapacitor devices.
## 2 Materials and Methods
### Optimization Procedure Combining ANN and GA
#### 2.4.2 Performance Optimization of Jet-Cleaning Using GA  
This study developed a collaborative framework integrating computational fluid dynamics (CFD), artificial neural networks (ANN), and genetic algorithms (GA) to optimize jet-cleaning performance. Leveraging the trained surrogate ANN model, this work employs the GA to enhance jet-cleaning performance metrics, with the optimization workflow detailed in Figure 5.  
The GA workflow initiates with the generation of an initial population comprising randomly generated individuals, where each individual represents a potential solution within the design space. Subsequently, the developed surrogate ANN model evaluates the fitness of each individual. Individuals exhibiting superior fitness values are selected as parents, from which offspring are generated via crossover and mutation operators. This iterative process progressively enhances the solution quality, as offspring inherit advantageous genetic traits from parent populations, thereby achieving performance improvements across generations.  
The genetic algorithm (GA) process is executed utilizing the trained surrogate ANN model, with optimization objectives to maximize the peak wall shear stress and effective action area during jet-cleaning while minimizing water consumption. The genetic algorithm (GA) parameters were configured as follows: population size of 200, maximum generations  
Figure 5: The optimization workflow via an integrated 3D CFD-ANN-GA approach.  
of 150, crossover fraction of 0.85, mutation rate of 0.01, and the fitness function deviation is \(1.0\times 10^{-4}\). These parameters are systematically tabulated in Table 5.
###### Abstract  
This paper proposes a hybrid methodology for portfolio optimization by integrating the data envelopment analysis (DEA) model with the mean semivariance (MSV) framework. The goal is to construct portfolios that achieve targeted returns while minimizing downside risk. The methodology comprises two stages: (1) identifying efficient stocks through DEA, where semivariance and beta (\(\beta\)) are employed as input risk metrics and the expected return serves as the output, and (2) determining optimal portfolio weights through the MSV model, solved using artificial neural networks (ANNs) and evolutionary algorithms. The empirical results demonstrate that portfolios optimized with ANNs exhibit significantly lower risk compared to those derived from evolutionary algorithms, highlighting the superiority of ANN-based approaches in balancing risk and return under the proposed framework. This study underscores the potential of hybrid DEA-MSV models enhanced by machine learning techniques for advanced portfolio management.  
Keywords:optimal portfolio; MSV; DEA; artificial neural networks; NSGA2; SPEA2 _articles_
## 3 Laboratory Study
### Testing
#### 3.3.6 Differential Scanning Calorimetry (DSC) Test  
Another technique commonly used to investigate the thermal behavior of materials is Differential Scanning Calorimetry (DSC). This technique provides valuable information about the physical, chemical, and thermal properties of materials by measuring the heat absorbed or released during a controlled heating or cooling process. DSC works on the principle of differential heat flow measurement, where a sample and a reference material are subjected to the same controlled temperature program. Any difference in heat flow between the sample and reference is measured and plotted as a function of temperature. The DSC test was performed on 5-10 mg of material in an aluminum hermetic pan. A nitrogen (N\({}_{2}\)) atmosphere with a flow rate of 50 mL/min was maintained throughout the test using the DSC2500 instrument from TA Instruments. The samples were prepared by cutting them into small pieces and placed in a pan. The pan and the pierced lid were crimped using a press.  
The samples were subjected to four (4) temperature cycles, ranging from \(-\)80 to 160 \({}^{\circ}\)C, to accurately characterize their thermal properties (Figure 4). The 1 st cycle aimed to facilitate proper contact between the bitumen sample and the pan by softening the bitumen. To achieve this objective, the samples were subjected to a temperature increase from room temperature to 130 \({}^{\circ}\)C, with a heating rate of 20 \({}^{\circ}\)C/min. The 2 nd cycle involved a temperature decrease to \(-\)80 \({}^{\circ}\)C, with a rate of 10 \({}^{\circ}\)C/min, followed by an increase to 160 \({}^{\circ}\)C. This cycle aimed to eliminate any thermal history in the samples. In the 3 rd cycle, the temperature again dropped to \(-\)80 \({}^{\circ}\)C to eliminate the cold crystallization effect. The last and 4 th cycle had a slightly different setup from the others. As reported in the [PERSON] et al. (2021) research, their study showed that Temperature Modulation-DSC (TM-DSC) is a more accurate technique than Temperature Linear-DSC (TL-DSC) for analyzing bitumen [59]. They also found that TM-DSC can detect the glass transition (T\({}_{\mathrm{g}}\)) region in bitumen with high precision. As a result, for the 4 th cycle, the TM-DSC protocol was set up with a heating rate of 5 \({}^{\circ}\)C/min, with \(\pm\)0.5 \({}^{\circ}\)C modulation every 80 s.
## 2 Proposed System
### Sensors and Embedded System Hardware
#### 2.2.5 RS485 Modbus to WiFi Converter  
As described earlier, the JXCT soil sensor supports the Modbus protocol. Thus, it can directly connect to the SCADA server, a Haiwell IoT cloud HMI in this case. However, since the wireless (WiFi) communication between the sensor and the HMI is established, the RS485 to WiFi converter is required. In this work, we use the Elfin-EW11 as the converter from the RS485 Modbus RTU to WiFi Modbus TCP. This device allows a simple and easy configuration via a Web page. The Elfin-EW11 specifications are given in Table 6.
## 2 System Description
### Clarke and Park Transformations  
#### 2.2.1 Clarke Transformation  
The Clarke mathematical transformation, also called the alpha-beta transformation, transposes a three-phase system into an orthogonal system [15]. The transformation is defined mathematically by the relation (2):  
\[\left[\begin{array}{c}v_{\alpha}\\ v_{\beta}\end{array}\right]=\left[\begin{array}{cc}\frac{2}{3}&\frac{-1}{3} &\frac{-1}{3}\\ 0&\frac{1}{\sqrt{3}}&\frac{-1}{\sqrt{3}}\end{array}\right]\cdot\left[\begin{array} []{c}v_{a}\\ v_{b}\\ v_{c}\end{array}\right] \tag{2}\]  
where the inverse transformation is defined by the statement (3):  
\[\left[\begin{array}{c}v_{a}\\ v_{b}\\ v_{c}\end{array}\right]=\left[\begin{array}{cc}1&0\\ \frac{-1}{2}&\frac{\sqrt{3}}{2}\\ \frac{-1}{2}&\frac{-3}{2}\end{array}\right]\cdot\left[\begin{array}{c}v_{a} \\ v_{\beta}\end{array}\right] \tag{3}\]  
Considering that the sinusoidal variables \(\omega\), \(\upsilon\), and \(\upsilon\) describe a balanced three-phase system, they will have the expressions [15]:  
\[v_{a}=V_{a}\cdot\sin\omega t \tag{4}\]  
\[v_{b}=V_{b}\cdot\sin\left(\omega t-\frac{2\pi}{3}\right) \tag{5}\]  
\[v_{c}=V_{c}\cdot\sin\left(\omega t-\frac{4\pi}{3}\right) \tag{6}\]  
\[V_{a}=V_{b}=V_{c} \tag{7}\]  
Figure 2: The topology of single-phase inverter.  
Under these conditions, by substituting (4)-(7) in Equation (2), the following relations can be expressed [15]:  
\[v_{a}=v_{a}=V_{a}\sin\omega t \tag{8}\]  
\[v_{\beta}=V_{a}\cdot\sin\Bigl{(}\omega t-\frac{\pi}{2}\Bigr{)}=-V_{a}\cdot\cos\omega t \tag{9}\]  
In a single-phase system, there are only one of the three sinusoidal variables. Next, this unique variable is assumed to be va, and is part of a permanently balanced three-phase imaginary system. Because the alpha-beta transformation is specific to three-phase systems, Equations (8) and (9), under the assumed conditions, describe a way to obtain the two orthogonal variables \(\
u\alpha\) and \(v_{\beta}\) in the case of a single-phase system [15].
## 3 Results and Discussion
### Linear Discriminant Analysis (LDA)  
Linear discriminant analysis is a statistical method that can determine which group the sample belongs. This method maximizes the variance between categories and minimizes the variance within categories. Figure 4 shows the linear discriminant analysis results for different feature vectors on fish meal samples of different qualities. As shown in the figure, the results of linear discriminant analysis were distinctly better than those of principal component analysis. PCA only fits a handful of samples at a time. If the number of fish meal samples to be investigated increases, the fish meal samples with different storage times will become crowded and not easily distinguished in the PCA chart. The distribution structures of all linear discriminant analyses were particularly similar, due to similar data structures. Although fresh samples were not easily distinguished because the first two discriminant functions were applied in the linear discriminant analysis chart, through the discriminant analysis results of the five discriminant functions. The accuracy rate of the linear discriminant analysis, using integral values as feature vectors, was 90%, and the accuracy rate of linear discriminant analysis, using the wavelet energy values as feature vectors was 85.6%, and the accuracy rate of linear discriminant analysis, using the maximum gradient values as feature vectors was 56.7%. The correct rate of linear discriminant analysis using the average differential value as feature vector was 89.4%, the correct rate of linear discriminant analysis using the relation steady-state response average value as feature vector was 93.9%, and the correct rate of linear discriminant analysis using the variance value as feature vector was 92.8%. Therefore, the accuracy rate using the relation steady-state response average value to evaluate fish meal samples with different qualities was the highest, and the accuracy rate using the maximum gradient value to analyze fish meal samples with different qualities was the lowest.  
Figure 3: Principal Component Analysis (PCA) analysis results for different characteristic values (integral value (**a**); wavelet energy value (**b**); maximum gradient value (**c**); average differential value (**d**); relation steady-state response average value (**e**); and variance value (**f**)).