esa-sceva/satcom-chunk-collection huggingface dataset:
# Hybrid Backbone-Based Deep Learning Model for Early Detection of Forest Fire Smoke
## 4 Experimental Results  
The training results of five Yolov8 models, five Yolov5 models, and twenty hybrid Yolov5 models with modified backbone structure, run with an improved smoke dataset, are listed in Table 2. When Table 2 is examined, one can see that the model with the highest precision value is the proposed model based on Yolov5-x with the ResNet50 backbone structure, at 98.8%. The models that follow this model with 98.2% and 98.0% precision values are the Yolov5-m model with the backbone structure replaced by MobileNetV3s, and the Yolov5-n model with the backbone structure replaced by ResNet50. The original Yolov5-l model had the highest recall value of 98.0% among the 30 models. The models that follow the Yolov5-l model in terms of recall value are the Yolov5-m model (97.3%), with the backbone structure modified by ResNet50, and the Yolov5-x-based proposed model (96.7%) with the backbone structure modified by ResNet50. The model with the highest F1 value (97.7%), which allows for precision and recall values to be displayed on a single value, is the recommended model with a modified backbone structure. The models that follow this model in terms of F1 are the original Yolov5 model and Yolov5-n model with the backbone structure modified by ResNet50, with values of 97.3% and 97.1%, respectively. The model with the highest mAP value for the training process was the original Yolov5-m model(98.8%). This model is followed by the Yolov5-n and the original Yolov5-l models, whose backbone structures have been modified with ResNet50, with a mAP value of 98.6%.5. The testing process was carried out with a test dataset consisting of image data that the models had never seen. The results recorded during the testing process are listed in Table 3. Among the models, the original Yolov5-n model, with a precision value of 95.5%, yielded the highest precision. Models that follow the Yolov5-n model as the precision value are the recommended model, whose backbone structure has been replaced with ResNet50. In addition, the Yolov5-x model, whose backbone structure was replaced with MobileNetV3s, yielded a result of 95.0%. The Yolov5-m model, whose backbone structure was replaced with ResNet50, yielded the highest result with a recall value of 96.7%. This model was followed by the proposed model, whose backbone structure was replaced with ResNet50 with a recall value of 96.4%. In addition, the model that yielded the best result in terms of F1 value, at 95.8%, was again the recommended model with its backbone structure. The models that follow this model in terms of F1 value are the original Yolov5-n model (95.3%) and the Yolov5-m model (95.2%), whose backbone structures were modified with ResNet50. Finally, the model with the highest mAP value of 96.9%, which allowed us to interpret the performance of the model in the best way, was the recommended model whose backbone
## 4 Results
### Zone II: Classopollis-Araucariacites-Bisacates Assemblage (44.6-18.5 m)  
Zone II is represented by ten analyzed samples, five of which had abundant spores and pollen. Gymnosperm pollen dominated (85.45-92.86%, avg. 89.58%), whereas fern spores were sparse (7.14-14.55%, avg. 10.70%).  
Although _Classopollis_ (mainly _Classopollis classoides_) remained predominant among the gymnosperm pollen, it showed a significant depletion (18.18-39.45%, avg. 27.66%). The content of _Araucariacites_ (mainly _Araucariacites australis_) of the Araucariaceae family increased significantly to proportions ranging from 15.45% to 31.25%, with an average of 23.35%. _Jiahepollis_ and _Callialasporites_ showed increasing trends, with percentages ranging from 2.59% to 19.09% and 3.45% to 8.18%, respectively, and averages of 9.57% and 5.17%, respectively. Identified _Jiahepollis_ species include _Jiahepollis verus_, _Jiahepollis flexuosus_, and _Jiahepollis scutellatus_. Bisaccates showed an increasing trend, with proportions ranging from 15.60% to 25.89%, with an average of 19.84%. Among them, Bisaccates with two well-developed sacs included _Alisporites_ (0-1.82%, avg. 0.79%), _Pinusopellenties_ (0-8.62%, avg. 3.10%), _Cedriytes_ (2.68-5.45%, avg. 3.67%), _Erliampollis_ (0-3.64%, avg. 1.55%), _Picea-pollienties_ (0-1.79%, avg. 0.53%), _Podocarpipilites_ (0-2.75%, avg. 1.26%), and _Parcisporites_ (0-1.82%, avg. 0.70%). Bisaccates with two incomplete sacs included _Protopinus_ (0-0.91%, avg. 0.54%), _Protopica_ (0-0.89%, avg. 0.18%), _Paleoconiferus_ (0-14.29%, avg. 5.60%), and _Protopoviretus_ (0-3.57%, avg. 1.67%). In addition, gymnosperm pollen contained small amounts of _Psophosphaera_, _Inaperturopolenties_, _Anizonosaccites_, _Perinopolenties_, _Cycadopites_, _Epidripies_, and _Chasmatosporites_.  
Fern spores had low abundances, ranging from 8.26% to 14.55%, but their types were relatively diverse. The fern spores observed included _Punctatisporites_, _Toroisporis_, _Undulatisporites_, _Aratrisporites_, _Dictyophyllidites_, _Verrucospirites_, _Retivernicispora_, _Sphagnum-sorites_, _Appendicisporites_, and _Cicatricosisporites_.
# Hepatic Reirradiation for Patients with Recurrent Hepatocellular Carcinoma  
[PERSON]  
1 Department of Radiation Oncology, Taipei Medical University Hospital, Taipei 110, Taiwan 12 Graduate Institute of Biomedical Materials and Tissue Engineering, College of Biomedical Engineering, Taipei Medical University, Taipei 110, Taiwan 2*1 Department of Radiation Oncology, Taipei Medical University Hospital, Taipei 110, Taiwan 2*1 Department of Radiation Oncology, Taipei Medical University Hospital, Taipei 110, Taiwan 12 Graduate Institute of Biomedical Materials and Tissue Engineering, College of Biomedical Engineering, Taipei Medical University, Taipei 110, Taiwan 2*1 Department of Radiation Oncology, School of Medicine, Taipei Medical University Hospital, Taipei 110, Taiwan 2*1 Department of Radiation Oncology, School of Medicine, College of Medicine, Taipei Medical University, Taipei 110, Taiwan 12*1 Department of Radiation Oncology, Taipei Medical University Hospital, Taipe  
little liver reserve, tumor numbers, tumor location and portal vein tumor thrombosis (PVTT) are common contraindications against the local therapies mentioned above. A liver transplant is the solution for certain conditions as so; however, the difficulty of the surgery and the availability of liver donation limit the clinical use [4].  
Recent improvements in radiotherapy (RT) have established RT in treating HCC, especially for larger tumors, tumors close to the diaphragm or in the presence of PVTT [4]. Advancing RT techniques, from traditional three-dimensional conformal radiotherapy (3D-CRT) to intensity-modulated radiotherapy (IMRT), along with use of image-guided radiotherapy (IGRT), respiratory motion management and even stereotactic ablative radiotherapy (SABR), make it feasible to deliver higher radiation dose to the target and constrain lower doses to the normal liver tissue. The progress results in better tumor control with fewer toxicities [1; 5]. For HCC that recurs after RT and is still contraindicated against other local therapies, several studies have discussed the feasibility of repeated irradiation to the liver. One Korean study retrospectively presented the feasibility of using reirradiation to treat recurrent HCC in a 45 patients with the traditional 3D-CRT technique, while another Taiwanese study categorized liver function as a predicting factor for radiation-induced liver disease (RILD) after hepatic reirradiation for HCC [6; 7]. Massachusetts General Hospital (MGH) also demonstrated a safe reirradiation to either primary or secondary hepatic malignancies with only 4.1% of patients developing RILD [8]. In 2020, Dr. [PERSON] concluded a treatment recommendation for liver reirradiation through a case-based discussion with four radiation oncology experts [9]. Building on the research mentioned above, we investigated the treatment outcome of reirradiation of recurrent HCC; meanwhile, survival and toxicity were also reviewed in this retrospective study.
# A Hybrid Handover Scheme for Vehicular VLC/RF Communication Networks
## 3 Hybrid Handover Scheme
### The Time Discretization  
Uniformization is applied to transform the continuous time Markov chain into a discrete one, so that the formulated MDP is more analytically tractable.  
Let \(v_{i,p(i)}\) denote the total transition rate for state \(i=(s,s^{\prime},h)\) with action \(p(i)\) (e.g., \(v_{(\text{On},\text{Off},\text{1}),p(i)}=\lambda+\gamma_{1}+\hat{\alpha}_{12} +\mu_{(\text{On},\text{Off},1)}\) ) and \(v_{m}\) denotes the uniform transition rate, which is the maximum of all the total transition rates, thus given by  
\[v_{m}=\max_{i,p(i)}v_{i,p(i)} \tag{14}\]  
Define the discrete time transition probability for state \(i=(s,s^{\prime},h)\) with action \(p(i)\) as \(\hat{\Pr}_{i\to i^{\prime}}\{i,p(i)\}\), which can be derived by [26]  
\[\hat{\Pr}_{i\to i^{\prime}}\{i,p(i)\}=\left\{\begin{matrix}\frac{\bar{v}_{i,p( i)}}{\bar{v}_{m}}\hat{\Pr}_{i\to i^{\prime}}\{i,p(i)\}&\text{if}&\text{i} \
eq\text{i}^{\prime}\\ 1-\frac{\bar{v}_{i,p(i)}}{\bar{v}_{m}}&\text{if}&\text{i}=\text{i}^{\prime} \end{matrix}\right. \tag{15}\]  
Then, the cost after uniformization, denoted by \(\hat{g}(i,p(i))\), is reformulated as  
\[\hat{g}(i,p(i))=\frac{1}{\beta+v_{m}}g_{1}(i,p(i))+g_{2}(i,p(i)), \tag{16}\]  
where \(\beta\) is a parameter related to the discounted factor \(\alpha\) as \(\alpha=v_{m}/(\beta+v_{m})\)[26]. \(\alpha\) is the discounted factor of the proposed MDP, which is close to but smaller than 1.
## 3 Results and Discussion
### Influence of Radical Species  
To realize the contribution of the oxidizing species (SO\({}_{4}\)\({}^{\bullet-}\) and HO\({}^{\bullet}\)) in the efficiency of UV/PS and photo-Fenton processes, two alcoholic radical scavengers, ethanol (EtOH) and tert-butyl alcohol (t-BuOH), were added to TA aqueous solutions. So, alcohol with alpha H (EtOH) can quench both hydroxyl and sulfate radicals; whereas, alcohol with no alpha H (t-BuOH) can mainly scavenge hydroxyl radicals and poorly react with sulfate radicals. In this study, alcohol (EtOH, t-BuOH) concentration used was 50 mM. Figure 3 illustrates the influence of EtOH and t-BuOH as radical scavengers on TA degradation efficiency during UV/PS and UV/H\({}_{2}\)O\({}_{2}\)/Fe\({}^{2+}\) processes. As Figure 3 shows, TA degradation was significantly decreased by the addition of alcohols in both processes. The degradation of TA in UV/PS process was 96% without any radical scavenger (see Figure 3a). However, the addition of EtOH and t-BuOH has reduced the efficiencies to 11% and 60%, respectively, within 60 min, compared to the degradation observed in the absence of both scavengers. The results confirmed that the addition of EtOH has diminished the degradation efficiency much more than the same amount t-BuOH.  
These observations imply that in the case of t-BuOH addition, hydroxyl radical HO\({}^{\bullet}\) was scavenged and TA was degraded by sulfate radical SO\({}_{4}\)\({}^{\bullet-}\), while in the case of EtOH addition, both SO\({}_{4}\)\({}^{\bullet-}\) and HO\({}^{\bullet}\) were scavenged, which lead to a low TA degradation yield. In order to evaluate the contribution of HO\({}^{\bullet}\) radicals into TA degradation, t-BuOH was introduced into the photo-reactor during photo-Fenton degradation of TA. As can be seen in Figure 3b, the addition of 50 mM t-BuOH decreased TA degradation yield from 98% (without scavengers) to 16% after 60 min reaction. This confirms that TA degradation occurs mainly via chemical oxidation with hydroxyl radicals during photo-Fenton process.  
These results confirmed that the higher aromatics removals obtained in the previous experiments, when US/PS and photo-Fenton processes were used, is mainly due to the oxidation of TA and its aromatics intermediates by radical oxidizing species (SO\({}_{4}\)\({}^{\bullet-}\) and HO\({}^{\bullet}\)). Furthermore, it is clear that when UV/PS process is used, not only sulfate radicals, SO\({}_{4}\)\({}^{\bullet-}\) contribute in the degradation of TA, but also hydroxyl radicals, HO\({}^{\bullet}\) are involved in TA degradation since the addition of EtOH and t-BuOH decreased TA degradation yield. Considering that t-BuOH is generally used as HO\({}^{\bullet}\) scavenger and EtOH can scaveng both SO\({}_{4}\)\({}^{\bullet-}\) and HO\({}^{\bullet}\) radical species, it can be concluded that 60% of TA molecules are degraded by HO\({}^{\bullet}\) radicals, and only 30-40% of TA molecules are degraded by SO\({}_{4}\)\({}^{\bullet-}\) radicals.
## 3 Experimental Design
### NPSIR Analysis  
In order to calculate the NPSIR, as shown in Equation (4), pixel count was executed in conjunction with the optical power, the Silicon responsivity, and the Bayer Filter response. Using an optical spectrum analyzer, the optical power emitted by each LED of the board in each color was obtained; Figure 10 shows the particular case of LED1 (top left in Figure 4) turned on in the different colors. Theoretical Bayer Filter response (Figure 11) and silicon responsivity (Figure 12) of the selected camera were obtained from the sensor data sheet [18; 19]. Since the data of the optical power were determined by sampling wavelengths from 385 nm to 745 nm with steps of 5 nm; the NPSIR was calculated using a numerical integration of Equation (4), yielding as shown in Equation (5).  
\[NPSIR_{dB}=20\cdot log\bigg{[}\frac{Pix_{S}}{Pix_{I}}\cdot\frac{\sum_{\lambda= 385}^{745}\bigg{(}P_{I}(\lambda)R(\lambda)F_{S}(\lambda)\bigg{)}}{\sum_{\lambda= 385}^{745}\bigg{(}P_{S}(\lambda)R(\lambda)F_{S}(\lambda)\bigg{)}}\bigg{]} \tag{5}\]  
Figure 11: Webcam Bayer Filter Response for Blue, Green and Red respectively data extracted from [18].  
Figure 10: Optical Power emitted by the top left LED of the designed board turned ON in Blue, Green and Red respectively, data obtained using an optical spectrum analyzer.
## 6 The Road Ahead
### Unikernels--The Overlooked Deployment Unit?  
Operating system virtualization based container technologies have massively influenced the resource utilization optimization trend. However, containers are not about virtualization from a cloud application deployment point of view. They are about a standardized and self-contained way to define deployment units. However, are containers the only solution and the most resource efficient solution already existing? The answer is no, and roads ahead might follow directions with the same intent to define standardized and self-contained deployment units but with better resource utilization.  
One option would be unikernels. A unikernel is a specialized, single address space machine image constructed via library operating systems. The first such systems were Exokernel (MIT Parallel and Distributed Operating Systems group) and Nemesis (the University of Cambridge, University of  
Figure 9: Serverless architectures result in a different and less centralized composition of application components and backend services compared with classical tiered application architectures.  
Glasgow, Swedish Institute of Computer Science and Citrix Systems) in the late 1990s. The basic idea is that a developer selects a minimal set of libraries which correspond to the OS constructs required for their application to run. These libraries are then compiled with the application and configuration code to build sealed, fixed-purpose images (unikernels) which run directly on a hypervisor or hardware without an OS. Thus, unikernels are self-contained deployment units like containers we investigated in Section 5.1.2 with the advantage to avoid a container overhead, a container runtime engine, and a host operating system (see Figure 5). Thus, interesting aspects to investigate on the road ahead would be:  
* Because unikernels make operating systems and container runtime engines obsolete, this could further increase resource utilization rates.
* FaaS platforms workers are normally container based. However, unikernels are a deployment option as well. Interesting research and engineering directions would be how to combine unikernels with FaaS platforms to apply the same time-sharing principles.  
However, although there is research following the long-term trend to improve resource utilization [50; 51], most cloud computing-related unikernel research [52; 53; 54; 55] mainly investigates unikernels as a security option to reduce attack surfaces (which are increased by serverless and microservice architectures as we have seen in Section 5.2). However, the resource optimization effect of unikernels might be still not aware to cloud engineers. Other than container technology, unikernel technology is not hyped.
## 2 3-DOF Chatter Stability Model  
### Milling System for a Thin-Walled Cylinder  
During the bull-nose end milling process of thin-walled cylinders, both the cutter and workpiece are regarded as flexible. The dynamic milling system for a thin-walled cylinder is demonstrated in Figure 1, which can be delineated by a 3-DOF mass-spring-damper model. In the \(x\)-axis and \(y\)-axis directions, a 2-DOF dynamic model is employed to delineate the cutter, while an absolutely rigid body is applied to assume the workpiece. In the \(z\)-axis direction, a 1-DOF dynamic model is employed to delineate the workpiece, while an absolutely rigid body is applied to assume the cutter. \(O\)-\(xyz\) stands for the cutter coordinatesystem (CCS). The origin \(O\) is centered at the end of the cutter. The workpiece feed direction is specified as the \(y\)-axis. The cutter axis direction is specified as the \(z\)-axis. The right-hand rule is employed to identify the \(x\)-axis. Based on the previous study [29], the cutting contact point \(P\) is identified according to the eccentric amount from the cutter axis to the centerline of the workpiece.  
The dynamic equation of the 3-DOF bull-nose end milling system is established as follows:  
\[\begin{bmatrix}m_{x}&0&0\\ 0&m_{y}&0\\ 0&0&m_{z}\end{bmatrix}\begin{bmatrix}\ddot{x}_{c}(t)\\ \ddot{y}_{c}(t)\\ \ddot{z}_{w}(t)\end{bmatrix}+\begin{bmatrix}c_{x}&0&0\\ 0&c_{y}&0\\ 0&0&c_{z}\end{bmatrix}\begin{bmatrix}\dot{x}_{c}(t)\\ \dot{y}_{c}(t)\\ \ddot{z}_{w}(t)\end{bmatrix}+\begin{bmatrix}k_{x}&0&0\\ 0&k_{y}&0\\ 0&0&k_{z}\end{bmatrix}\begin{bmatrix}x_{c}(t)\\ y_{c}(t)\\ z_{w}(t)\end{bmatrix}=\begin{bmatrix}F_{x}\\ F_{y}\\ -F_{z}\end{bmatrix} \tag{1}\]  
where \(m_{x}\), \(c_{x}\), and \(k_{x}\) denote the modal mass, damping, and stiffness of the cutter, respectively, along the \(x\)-axis direction. \(m_{y}\), \(c_{y}\), and \(k_{y}\) denote the modal mass, damping, and stiffness of the cutter, respectively, along the \(y\)-axis direction. \(m_{z}\), \(c_{z}\), and \(k_{z}\) denote the modal mass, damping, and stiffness of the workpiece, respectively, along the \(z\)-axis direction. \(x_{c}(t)\) and \(y_{c}(t)\) denote the cutter displacements along the \(x\)-axis and \(y\)-axis directions, respectively. \(z_{w}(t)\) denotes the workpiece displacement along the \(z\)-axis direction. \(F_{x}\), \(F_{y}\), and \(F_{z}\) denote the cutting forces along the \(x\)-axis, \(y\)-axis, and \(z\)-axis directions, respectively.  
Figure 1: 3-DOF dynamic model in bull-nose end milling of a thin-walled cylinder.
## 2 Cybersecurity Challenges and Threats in X
### Security Threats in X
#### 2.1.3 Social Threats  
Regarding online social threats, attackers can utilize the social relationship aspect of X to maliciously engage with different user groups, such as minors and corporate employees. For example, an attacker may manipulate minors by expressing compassion and offering online gifts or money. Their reasons for doing so can range from blackmail, distributing pornography, conducting cyberbullying, and espionage [4]. In this section, we outline the various social dangers that take advantage of different online social relationships for different motives.
* Corporate espionage  
Corporate espionage can employ automated social engineering tactics through X. By utilizing X as a tool, a social engineer can obtain valuable information, such as the job title, email, and complete name of employees, without relying on traditional social engineering methods and infiltrating the company. A study by [36] describes a method of using social networking sites (X) to execute a social engineering attack. They demonstrated that by utilizing X, an attacker can gather information about an employee within a targeted organization in an automated fashion, which can then be utilized for a successful social engineering attack [37].
* Cyberbullying and cyber-grooming  
Cyberbullying is the repeated online harassment of an individual, while cyber-grooming involves an adult forming an emotional bond with a child to facilitate sexual abuse. Children are especially susceptible to these online threats due to their age [38]. Teenagers facing cyberbullying are at an increased risk of depression. Online predators often exploit this vulnerability by posing as caring individuals, luring victims with gifts and financial incentives. Security experts report that such predators have targeted thousands of students globally through deceitful practices. A notable case is that of [PERSON], whose tragic suicide highlighted the severe consequences of cyberbullying, as the perpetrator created a fake online profile for manipulation and cyber-grooming [39].
* Cyberstalking X users have the option to reveal their personal details such as contact information, home address, location, and schedule on their X profile. However, this information can be vulnerable to exploitation by malicious individuals for cyberstalking purposes. For example, an attacker can blackmail their victim through phone calls or instant messages on X. Additionally, users often share location information through their photos, which attackers can gather and use for harmful cyberstalking attacks. Researchers reviewed the effects of cyberstalking on German X users on StudiVZ. They emphasized that cyberstalking could harm the mental well-being of X users and should be regarded as a significant danger to ensure a safe and secure environment on the platform.