esa-sceva/satcom-chunk-collection huggingface dataset:
# Pathophysiologyiology and Treatment of Soriasis: From Clinical Practice to Basic Research
## 3 Pathogenesis of Psoriasis
### Cell Signaling Pathways Related to Soriasis
#### 3.3.1 Interleukin-23/T Helper Cell (IL-23/Th17) Axis  
Under pathogenic stimuli, myeloid dendritic cells (DCs) become activated and produce excessive levels of inflammatory cytokines, such as IFN-\(\alpha\), tumor necrosis factor-alpha (TNF-\(\alpha\)), and IL-6. These cytokines, in turn, promote the increased secretion of IL-12 and IL-23 [41]. In response to IL-23, Th17 cells produce high levels of IL-17, which acts on keratinocytes, leading to epidermal hyperplasia, activation of innate immune responses, recruitment of leukocytes to the skin, and further production of pro-inflammatory cytokines such as IL-1\(\beta\), IL-6, and IL-8 [42]. Blocking IL-17 can disrupt this pathological cycle, effectively reducing inflammation and alleviating clinical symptoms of psoriasis [43].  
3.2 Macrophage Inflammatory Protein 3 Alpha/Chemokine (C-C Motif) Ligand 20-C-C Chemokine Receptor 6(MIP-3\(\alpha\)/CCL20-CCR6) Pathway  
The CCL20/CCR6 axis has long been associated with inflammation. CCL20 (also known as MIP-3\(\alpha\)) acts as a chemotactic factor that, through binding with its receptor CCR6, is crucial for transporting IL-17A-producing T cells from the bloodstream to the skin during the progression of psoriasis [44]. CCL20 is primarily produced by activated keratinocytes, while CCR6 is predominantly expressed on dendritic cells (DCs) and T cells, especially Th17 cells, serving as a marker for Th17 and regulating lymphocyte migration. In psoriatic skin lesions, keratinocyte activation leads to the upregulation of CCL20 expression, enhancing its binding to CCR6. This increased binding attracts more Th17 cells to the site, which produce cytokines like IL-17, further driving the IL-23/Th17 axis and perpetuating the inflammatory response in psoriasis [45].
## 1 Introduction  
Osteoarthritis (OA) of the knee is the most common result of arthritis that leads to pain, stiffness and decreased mobility, with this disease being one of the major causes of disability among non-institutionalized adults [1; 2]. OA is a process of cartilage degeneration that involves the immune system, in which local inflammatory responses are observed with the production of proinflammatory cytokines [3; 4]. The articular cartilage possesses limited reparative abilities and the associated osteochondral defects present in young patients generally do not heal but usually progress to degeneration of the surrounding cartilage [5]. There are a limited number of treatment options available to improve or reverse the process [3; 4]. Additionally, with the exception of joint replacement, the most common treatments for OA are not widely applied and can be associated with substantial adverse events, high costs or both [6].  
Mesenchymal stem cells (MSCs) have been proposed to possess potential in the cell-based treatment of cartilage lesions [7]. These cells show great promise as a therapeutic agent in regenerative medicine due to their multilineage potential, immunosuppressive activities, limited immunogenicity and relative ease of growth in culture [7]. Additionally, MSCs represent an autologous cell source that reduces the chance for rejection and disease transmission they are also less tumorigenic than their embryonic counterparts [8]. However, there are no studies that have quantitatively evaluated the degree of rehabilitation of an OA knee joint after injection or transplantation of MSCs.  
Mechano-regulation algorithms have been suggested to evaluate the possible relationship between mechanical stimulation and the differentiation of cells and tissues [9; 10; 11; 12; 13; 14; 15]. These phenomenological computational models, however, have several problems, particularly regarding the general simplification, such as loading condition and simplified geometry [9; 10; 11; 12]. Recently we studied to predict the mechanical properties of an optimum scaffold required for cartilage regeneration using three-dimensional knee joint developed from medical imaging and mechano-regulation theory [16].  
Therefor, the aim of the present study was to investigate cartilage regeneration in OA or cartilage defects based on the knee joint mechano-regulation of MSC differentiation theory using a realistic model based on three-dimensional (3D) medical imaging. This model considers the effect of mechanical stimuli on cell mitosis and death and it also incorporates the influence of the tissues on cell dispersal rate We hypothesized that the prediction of mechano-regulated tissue differentiation would differ with respect to the loading conditions.
## 4 Control System Design
### The Storage Battery Charger Controller
#### 4.3.1 The Fuzzy PID Controller  
In the literature, there is a lot of research on the PID controller based on some intelligent techniques using fuzzy logic principles [23, 24, 25]. A self-tuning fuzzy PID controller is proposed for this research. It uses the fuzzy inference system to tune the gains of the PID controller. This is accomplished by manipulating the error (\(\epsilon\)) and the derivative of the error (\(\Delta\epsilon\)). The block diagram of the ESS charge controller is shown in Figure 4b. The self-tuning fuzzy PID controller description is shown in Figure 4d.  
The idea is to implement the fuzzy rules and reasoning and then produce PID controller gains. It is well-known that the PID controller output is given by:  
\[y=k_{p}e+k_{i}\int e\ dt+k_{d}\frac{de}{dt} \tag{4}\]  
where (\(y\)) is the PID controller output, which is the reference current (\(l_{b\_ref}\)) for the ESS constant current control loop.  
The PID controller gains (\(k_{p}\), \(k_{i}\), \(k_{d}\)) are determined based on the measured error (\(\epsilon\)) and the difference of the error signal (\(\Delta\epsilon\)):  
\[\left[\begin{array}{c}e\\ \Delta\epsilon\end{array}\right]=\left[\begin{array}{c}i_{b}-i_{b\_ref}\\ e(t)-e(t-T)\end{array}\right] \tag{5}\]  
where (\(T\)) is the sampling rate and (\(i_{b}\), \(i_{b\_ref}\)) is the ESS current and reference current.  
The procedures of fuzzification of the controller parameters, fuzzy reasoning, and defuzzification will be explained in the following sections.
# Flying Ad Hoc Networks: A New Domain for Network Communications
## 2 State of the Art
### Propagation Models
First, we will derive from the measurements the best configuration of the antennas for an isolated WiFi communication device. This WiFi node will be latter embedded in the UAV to provide simultaneous communications in both the 2.4 GHz and the 5 GHz bands. Each band is dedicated to a U2U link and to a U2G link, respectively, as shown in Figure 1. Second, we will get the RP of the whole system: the WiFi device embedded in the UAV. From the results, we will quantify the effect (if any) of the UAV engines (revolutions per minute--RPM--and vibrations produced in chassis and antennas), the UAV propellers, and the UAV chassis on the RP. Third, in the light of these outcomes, we will show the conditions that should be met in order to assume that an UAV has an isotropic RP or (at least) a constant RP, and thus we provide a basis to simplify future related works.
# Estimating Forest Carbon Stock Using Enhanced ResNet and Sentinel-2 Imagery
## 4 Results  
### Performance Comparison  
To evaluate the effectiveness of the proposed Enhanced ResNet architecture for forest carbon stock estimation, we conducted a comprehensive comparison with three representative baseline models: Random Forest (RF), Gradient Boosting Decision Trees (GBDT), and Vision Transformer (ViT). All models were trained and tested on the same dataset splits described in the methodology section and evaluated using three widely adopted regression metrics: Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and the Coefficient of Determination (\(R^{2}\)).  
As shown in Table 2, the Enhanced ResNet achieved the best overall performance across all evaluation metrics. Specifically, it yielded an RMSE of 23.02 Mg/ha, an MAE of 17.15 Mg/ha, and an \(R^{2}\) of 0.773 on the test set. In contrast, the ViT baseline reached an RMSE of 29.55 Mg/ha and an \(R^{2}\) of 0.719. Compared to ViT, the Enhanced ResNetreduced RMSE by approximately 22.1% and improved \(R^{2}\) by 7.5%. Similar improvements were observed over the GBDT and RF models, with particularly significant reductions in MAE. These gains reflect the effectiveness of incorporating multiscale residual blocks, which enhance spatial feature representation, and Squeeze-and-Excitation (SE) modules, which enable adaptive weighting of informative spectral channels such as red-edge and SWIR bands.  
Overall, these results highlight the superior capability of the Enhanced ResNet in capturing complex spatial-spectral dependencies within high-resolution Sentinel-2 imagery, enabling more accurate and reliable estimation of forest carbon stocks in topographically and ecologically heterogeneous regions like the Liuchong River Basin. The consistent performance gain across all metrics suggests that deep learning models, when carefully designed to incorporate both spatial and spectral priors, can offer substantial advantages over traditional machine learning and transformer-based alternatives in ecological remote sensing applications.
## 2 Pharmacological Activities of Forskolin
### Cardiovascular Diseases  
Forskolin has a particularly beneficial effect on the cardiovascular system. It works via vasorelaxation, causing relaxation of smooth muscles in the walls of blood vessels, which results in increasing the overall volume of the circulatory system while maintaining the volume of blood. Thanks to this, it improves the blood circulation process, the blood supply to internal organs and increases the efficiency of the myocardium. In 1983, [PERSON] et al. reported the pharmacological effects of forskolin in cardiovascular diseases. They reported positive isotropic effects of forskolin in membrane preparations derived from failing and normal functioning human left ventricles [39]. Several clinical and preclinical studies have been carried out, which provide sufficient evidence for the involvement of forskolin in cardiovascular diseases. In isolates of guinea pig hearts, an increase in the forskolin dose resulted in amplified contractions. However, changes in the heart rate were low. The simultaneous increase in the coronary flow and oxygen consumption represents additional vasodilator effects of this drug on the coronary circulation [40]. A clinical study conducted by [PERSON] et al., in 1987, reported that 15 patients with dilated cardiomyopathy were administered with forskolin (10 \(\upmu\)g/kg) for a duration of 10 min in the first course of administration. The data were then compared with those data with dobutamine administration [41]. In the second course, 3 \(\upmu\)g/kg/min forskolin was administered for 10 min before and at the end of each infusion period where the heart rate was maintained constant by atrial pacing [41]. The study concluded that forskolin can inhibit the decrease of end-diastolic pressure in the left ventricle. [PERSON] et al. suggested, in their study, that the effect of forskolin lies in its ability to cause vasodilation. They further elaborated that the dose of forskolin needs to be high to produce positive effects in cardiovascular diseases, in addition to the decrease in the blood pressure and systematic vascular resistance observed [42].  
Figure 3: Potential modulation effect of forskolin based on [36]. ADP, adenosine diphosphate; ATP, adenosine triphosphate; cAMP, cyclic adenosine monophosphate; PKA, cAMP-dependent protein kinase; CFTR, cystic fibrosis transmembrane conductance regulator; CFTR-P, phosphorylated CFTR.
# An Analysis of Cloud Security Frameworks, Problems and Proposed Solutions
## 3 Cloud Security Frameworks
### NIST SP 800-144
#### 3.2.3 NIST Best Practice  
NIST Special Publication 800-144 outlines security and privacy principles for public cloud computing. Here are several NIST 800-144 best for protecting a cloud-based environment each steps given in Figure 3:  
1. Understand the Cloud Environment: Gain a thorough awareness of the cloud environment, including the types of cloud services utilized (e.g., Software as a Service, Platform as a Service, and Infrastructure as a Service) and the security and privacy issues that come with them.
2. Security and Privacy standards: Determine and record your organization's security and privacy standards. Data sensitivity, compliance needs, legal concerns, and other industry-specific standards are all part of this.
3. Selection and Evaluation of Cloud Service Providers (CSP): Examine possible CSPs' security capabilities and practices. Consider the CSP's security certifications, incident response methods, data encryption systems, and adherence to relevant standards.
4. Data Protection: Put in place suitable safeguards to secure data in the cloud. This involves encrypting sensitive data at rest and in transit, imposing least privilege access rules, and putting in place data backup and recovery methods.
5. Implement robust identity and access management (IAM) controls to guarantee that only authorized personnel have access to cloud resources. To efficiently manage user rights, employ multi-factor authentication, strong password rules, and role-based access controls (RBAC).
6. Secure Configuration Management: Securely configure cloud resources by adhering to industry best practices and CSP standards. Review and update setups on a regular basis to meet emerging threats and vulnerabilities.
7. Continuous Monitoring and Logging: Implement strong monitoring and logging methods in the cloud environment to follow activities and detect security occurrences. To discover possible risks and abnormalities, monitor system logs, network traffic, and user actions.
8. Incident Response and Recovery: Create and implement a cloud-specific incident response plan. Create protocols for identifying, reacting to, and recovering from security events. To ensure the plan's efficacy, test and update it on a regular basis.
9. Compliance and Governance: Ensure that appropriate legislation, standards, and contractual commitments are followed. To manage cloud security, create a governance structure that includes roles and duties, policies, and processes.  
Figure 3: NIST Core Functions.  
Security Awareness and Training: Provide ongoing security awareness and training to educate employees about cloud security risks and best. Foster a culture of security awareness and accountability within the organization.  
Each phase of the NIST architecture for securing cloud infrastructure necessitates the use of a variety of tools and technologies. Here, are some regularly used tools that might help with each step. Table 6 gives information about tools required to implement NIST framework
## 3 The Proposed Control Strategies
### Case 3: Circulating Currents with Harmonic Distortions  
In addition to the second-order harmonic, harmonic distortions with various frequencies are assumed as the worst-case scenario for circulating currents in this subsection.  
The considered circulating currents can be written as (12):  
\[i_{cirk}=I_{dc\_cir}+\sum\limits_{j=1,2,5,7}^{\infty}I_{mcirj}\cos\Bigl{(} \omega_{j}t+n\frac{2\pi}{3}+\alpha_{cirj}\Bigr{)} \tag{12}\]  
After some mathematical calculations with the help of relationships (1), (2), (6), and (12), the upper and lower arm currents are obtained as the following:  
\[\begin{array}{l}i_{u(l)k}=0.5\sum\limits_{j=1,3,7}^{\infty}I_{mj}\cos\Bigl{(} \omega_{j}t+n\frac{2\pi}{3}+\beta_{j}\Bigr{)}\pm\frac{i_{dc}}{3}\\ +I_{dc\_cir}\pm\sum\limits_{j=1,2,5,7}^{\infty}I_{mcirj}\cos\Bigl{(}\omega_{j} t+n\frac{2\pi}{3}+\alpha_{cirj}\Bigr{)}\\.\end{array} \tag{13}\]  
According to (13), all harmonic components of the circulating currents emerge in both the upper and lower arm currents. Using (3) and (4), the aforementioned upper and lower currents can be used to design the control laws in (14):  
Figure 3: Proposed control strategy under circulating currents with a dc component and second-order frequency sinusoid: (**a**) harmonic components of control laws and (**b**) the control laws.  
\[\begin{array}{l}v_{u(l)k}=\frac{v_{dc}}{2}-R_{l}\frac{i_{dc}}{3}-R_{l}l_{dc,cir}-L_{t}\frac{i_{dc}}{3}\\ \pm(0.5L_{t}+L)\sum\limits_{j=1,3,7}^{\infty}I_{mj}\omega_{j}\sin(\omega_{j}t+ n\frac{2\pi}{3}+\beta_{j})\\ +L_{t}\sum\limits_{j=1,2,5,7}^{\infty}I_{mcirij}\omega_{j}sin(\omega_{j}t+n\frac {2\pi}{3}+\alpha_{cirj})\mp(0.5R_{t}+R)\\ \times\sum\limits_{j=1,3,7}^{\infty}I_{mj}\cos(\omega_{j}t+n\frac{2\pi}{3}+ \beta_{j})-R_{t}\sum\limits_{j=1,2,5,7}^{\infty}I_{mcirij}\\ \times\cos(\omega_{j}t+n\frac{2\pi}{3}+\alpha_{cirj})\mp\sum\limits_{j=1,5,7}^ {\infty}\upsilon_{mj}\cos(\omega_{j}t+n\frac{2\pi}{3})\\ \end{array} \tag{14}\]  
Based on the achieved control laws (14), the proposed control strategy under circulating currents with harmonic distortions is expressed by Figure 4. As can be seen from Figure 4a, the proposed control laws are highly affected by the various harmonic components of the circulating currents. Two additional kinds of PI controllers are employed in this part to provide more appropriate control in such operating conditions.  
Figure 4: Proposed control strategy under circulating currents with harmonic distortions: (**a**) harmonic components of control laws and (**b**) the control laws.
## 1 Introduction
### Background and Motivation
The problem is formulated as the joint relay selection, spectrum allocation and power control optimization for two-hop D2D relay transmission underlay cellular networks. Both the first hop from D2D TX to D2D RS and the second hop from D2D RS to the D2D receiver (RX) are allowed to reuse the spectrum of CUEs to improve spectrum efficiency. Our objective is to find an approach to determine the optimal relay, spectrum and transmit power of TXs and RSs, which maximizes the EE of D2D links, while guaranteeing the QoS of all of the links. The problem is difficult to solve, as it not only includes continuous and discrete variables, but also involves four-dimensional resource allocation indicators [24]. It is a challenge to solve the problem with four-dimensional variables directly, since it is infeasible to achieve a solution in polynomial time for this NP-hard problem [25]. Besides, the transmit power of D2D TXs and RSs must be controlled to guarantee the QoS of UEs and achieve the maximum EE simultaneously, which also increases the complexity of the problem. Compared with our previously proposed works [11; 12; 26; 27], this work focuses on the multi-hop D2D links, which was not investigated before, and the optimization problem in this work is a four-dimensional matching among D2D pairs, relay selections and resource blocks (RBs) reused in the first and second hop of two-hop D2D links, which is much more complicated than the previous works. Since the problem involved in this work is a joint optimization problem with multidimensional variables, it cannot be solved by using the previously proposed algorithm directly. To give a tractable solution of this four-dimensional matching problem,based on the previously proposed three-dimensional matching algorithm, we proposed an iterative four-dimensional matching algorithm.
# Designing an Adaptive Underwater Visible Light Communication System +
###### Abstract  
The Internet of Underwater Things (IoUT) has attracted significant attention from researchers due to the fact that seventy percent of the Earth's surface is covered by water. Reliable underwater communication is the enabler of IoUT. Different carriers, such as electromagnetic waves, sound, and light, are used to transmit data through the water. Among these, optical waves are considered promising due to their high data rates and relatively good bandwidth efficiency, as water becomes transparent to light in the visible spectrum (400-700 nm). However, limitations such as link range, path loss, and turbulence lead to low power and, consequently, a low signal-to-noise ratio (SNR) at the receiver. In this article, we present the design of a smart transceiver for bidirectional communication. The system adapts the divergence angle of the optical beam from the transmitter based on the power of the signal received. This paper details the real-time data transmission process, where the transmitting station consists of a light fidelity (Li-Fi) transmitter with a 470 nm blue-light-emitting diode (LED) and a software-defined radio (SDR) for underwater optical communication. The receiving station is equipped with a Li-Fi receiver, which includes a photodetector with a wide field of view and an SDR. Furthermore, we use pulse position modulation (PPM), which demonstrates promising results for real-time transmission. A key innovation of this paper is the integration of the Li-Fi system with the SDR, while the system adapts dynamically using a servo motor and an Arduino microcontroller assembly. The experimental results show that this approach not only increases throughput but also enhances the robustness and efficiency of the system.  
Keywords:underwater visible light communication; pulse position modulation; Li-Fi; software-defined radio; power; bit error rate +
Footnote †: This article is a revised and expanded version of the paper published in [PERSON]; [PERSON]; [PERSON] Impact of transceiver alignment on a software-defined underwater visible light communication system. In Proceedings of the Asia-Pacific Conference on Communications (APCC), Bali, Indonesia, 5-7 November 2024.
# Comparison of Disturbance Compensators for a Discrete-Time System with Parameter Uncertainty
## 3 Overview of Disturbance Compensators
### Sliding Mode ControlDisturbance Compensator  
The sliding mode control disturbance compensator (SDC) was first proposed by [PERSON] et al., in [12]. Employing the popular reaching law Equation (16):  
\[s(k+1)=(1-q)s(k)-\xi\text{sign}(s(k)) \tag{16}\]  
the expression of SDC is generated as Equation (17):  
\[u_{d4}(k)=\sum_{i=2}^{k}\{s(i)-[(1-q)s(i-1)-\xi\text{sign}(s(i-1))]\} \tag{17}\]  
Considering Equations (16) and (17), a deduction can be generated as Equation (18):  
\[\sum_{i=2}^{k}\{s(i)-[(1-q)s(i-1)-\xi\text{sign}(s(i-1))]\}=Cf(k-1) \tag{18}\]  
It is observed from Equations (17) and (18) that the cumulative differences between the switching function \(s(i)\) and the reaching law \((1-q)s(i-1)-\xi\text{sign}(s(i-1))\) present the generalized disturbance at the \(k-1\) steps. Moreover, Equation (17) also implies that the SDC \(u_{d4}(k)\) is equivalent to the one-step delay estimation (OSDE) \(u_{d2}(k)\). Hence, if \(\text{C}|f(k)-f(k-1)|\leq\Omega_{2}\) holds, the compensation error of \(u_{d4}(k)\) satisfies \(\left|\overline{u}_{d4}\right|\leq\Omega_{2}\).  
The main disadvantage of SDC is that it is only valid for a slow time varying disturbance, just like the one-step delay estimation.