esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
**Objective:** To review the evidence regarding the clinical effect of spa therapy for rheumatic diseases, with particular attention given to association protocols between balneotherapy and rehabilitation interventions, and to support the literature research and studies' selection with lexical analysis. **Methods:** A lexical analysis was performed considering a list of words representing diseases and outcome measures linked to the theme studied in our review. Then, two independent researchers conducted a literature search on PubMed using the string employed for lexical analysis, including Randomized Controlled Trials regarding spa therapy's clinical effects on patients affected by rheumatic diseases published in the last 30 years. After the exclusion of works that did not meet the eligibility criteria, 14 studies were included in the final scoping review. **Results:** Spa therapy has shown a favourable effect on pain, function and quality of life in patients with Osteoarthritis, Fibromyalgiagia and Rheumatoid Arthritis. Different treatment modalities and types of water have demonstrated beneficial long-term clinical improvement. Furthermore, the association between thermal therapy and rehabilitation treatments has shown better clinical outcomes, probably due to the synergistic effect between the peculiar properties of the thermal waters and the therapeutic exercise program, if conducted in the same context. **Conclusions:** The combination of balneotherapy and rehabilitative interventions seems to be effective in ameliorating several outcomes in patients with rheumatic diseases. However, due to the wide variety of methodologies and interventions employed, these findings need to be further investigated. The lexical analysis should represent an auxiliary support for an extensive evaluation of scientific literature.  
balneotherapy; spa therapy; mud therapy; rheumatic disease; rehabilitation; therapeutic exercise; lexical analysis +
Footnote †: journal: _sciences_
# An Agent-Based Simulation of How Promotion Biases Impact Corporate Gender Diversity
## 5 Results
### Experiment 1: The Impact of Gender Biases in Promotion
First, we see that even though the promotion bias is a single parameter that works uniformly at each level, the successive promotions compound the effect, so that the gender imbalance is greatest at the executive level: after several simulated years, the executive level shows an imbalance of approximately 80% men when the bias is at 0.3 (Figure 4), and exceeds 90% men when the bias is at 0.5 (Figure 5). Second, increasing the bias has the effect of increasing the degree of imbalance, but also the speed with which the imbalance spreads through the organization: notice that with a bias of 0.3, the imbalance at the executive level builds gradually over a span of nearly 20 years, but when the bias is 0.5, the proportion of men at the executive level crosses the 80% mark in less than five years, and has essentially leveled off by year 10. Figure 4: Fluctuations in gender balance across all four levels during a 40-year simulation when the promotion bias is 0.3. Figure 5: Fluctuations in gender balance across all four levels during a 40-year simulation when the promotion bias is 0.5. Third, there is a surprising effect at the entry level: the percentage of female employees goes up as the promotion bias increases, even though there is no hiring bias, and even though women are being terminated more often than men because the promotion score influences separations. We can see in Figures 4 and 5 that women make up an increasing percentage of the entry-level population, reaching 60% when the bias is 0.5. The reason for this \"reverse imbalance\" is that men are being promoted at a much higher rate than women, so that women are being left behind. In reality, this is not uncommon in the real world: many industries have large numbers of women in entry-level positions, and women often describe the negative experience of being \"stuck\" while their male colleagues get promoted [53; 54]. This last observation illustrates another great aspect of agent-based simulations: unlike typical \"black-box\" models, with an agent-based simulation it is possible to dig into the detailed activities to understand the origin of observed macroscopic phenomena, i.e., emergent behaviors at the system level. Overall, the results of the first experiment show that, starting with very simple assumptions, we can capture some qualitative phenomena that match our observations of real-world companies: the presence of gender biases in promotions leads to an increasing gender imbalance at higher levels, and women being stuck in lower levels. In the next section, we show how manipulating both hiring and promotion biases can accurately replicate real-world data on gender imbalances observed for specific industries.
## 2 Theoretical Modeling
### Equations of Motion and Numerical Algorithm  
Using the finite element method, the equation of motion of the nonlinear rotor system can be expressed as shown below:  
\[M\ddot{u}+G\dot{u}+Ku=F^{nml}+F^{mbl}, \tag{2}\]  
\(M\), \(K\) and \(G\) can be easily obtained using the finite element method. \(F^{nml}\) denotes the nonlinear forces exerted by the SFD. \(F^{nub}\) denotes the unbalance force vector.  
The model consists of second-order nonlinear differential equations. The computational efficiency depends on both the numerical methods used and the total degrees of freedom of the rotor system. To strike a balance between accuracy and computational efficiency, fixed interface modal synthesis is utilized to reduce the dimensionality of the model and therefore its computational effort.  
Equation (2) can be rewritten as:  
\[\begin{bmatrix}M_{II}&M_{IJ}\\ M_{II}&M_{JJ}\end{bmatrix}\begin{bmatrix}\ddot{u}_{I}\\ \ddot{u}_{I}\end{bmatrix}+\begin{bmatrix}G_{II}&G_{IJ}\\ G_{II}&G_{JJ}\end{bmatrix}\begin{bmatrix}\dot{u}_{I}\\ \dot{u}_{I}\end{bmatrix}+\begin{bmatrix}K_{II}&K_{IJ}\\ K_{II}&K_{JJ}\end{bmatrix}\begin{bmatrix}u_{I}\\ u_{I}\end{bmatrix}=\begin{bmatrix}F_{I}^{nub}\\ 0\end{bmatrix}+\begin{bmatrix}0\\ F_{I}^{nont}\end{bmatrix}, \tag{3}\]  
where \(u_{I}\) is the linear DOFs where only unbalance forces act, while \(u_{I}\) represents the non-linear DOFs or the interface of DOFs in this work.  
A detailed description of the modeling and solving process can be found in Reference [21].  
\[q_{k}^{n+1}=S_{q}^{-1}\Big{(}\phi_{k}^{T}F_{I}^{nub\ n+1}-V_{q}u_{J}^{n+1}+W_{q} \Big{)} \tag{4a}\] \[\Big{(}S_{J}-V_{J}S_{q}^{-1}V_{q}\Big{)}u_{J}^{n+1}=F_{J}^{nont\ n+1}+\Big{(} \phi_{c}^{T}-V_{J}S_{q}^{-1}\phi_{k}^{T}\Big{)}F_{I}^{nub\ n+1}-V_{J}S_{q}^{-1} W_{q}+W_{J},\] (4b) with \[W_{q}=\overline{M}_{II}A_{q}^{n}+\overline{G}_{II}B_{q}^{n}+\overline{M}_{IJ}A_ {J}^{n}+\overline{G}_{IJ}B_{I}^{n} \tag{5a}\]\[W_{J}=\overline{M}_{JJ}A_{J}^{n}+\overline{G}_{JJ}B_{J}^{n}+\overline{M}_{JI}A_{q}^{n }+\overline{G}_{JI}B_{q}^{n} \tag{5b}\] \[S_{q}=a\overline{M}_{II}+b\overline{G}_{II}+\overline{K}_{II}\] (5c) \[S_{J}=a\overline{M}_{JI}+b\overline{G}_{JI}+\overline{K}_{JI}\] (5d) \[V_{q}=a\overline{M}_{IJ}+b\overline{G}_{IJ}\] (5e) \[V_{J}=a\overline{M}_{JI}+b\overline{G}_{II}\] (5f) \[F_{J}^{nonl~{}n+1}=F_{J}^{nonl~{}n}\Big{(}u_{J}^{n+1},\dot{u}_{J}^{n+1}\Big{)}. \tag{5g}\]  
In summary, the nonlinear model of the rotor system is established using the fixed interface modal synthesis method and the finite element method; then, an implicit time domain method based on the Newmark \(\beta\) method is applied to solve the equations of motion of the reduced system so that the dynamic characteristics can be obtained.
# Detection of Induced GNSS Spoofing Using S-Curve-Bias
## 4 Experiments
### Results of Ratio Method  
For the ratio method, Figure 7 shows the curve during an induced spoofing attack. The time length was also 294 s. The induced spoofing was also added at the 110-th s. We can see that the changing of different satellites were almost the same. Before 200 s, the changing amplitude of the ratio method was not obvious. Thus it was difficult to detect an induced spoofing attack before 200 s. After that, the ratio method was slowly changing.  
Figure 8 shows the ratio method curve of PRN 23. In Figure 8, the sold curve is the ratio test metric for PRN23. The different dashed curves are detection thresholds \(\gamma^{\prime}\) according to different false alarm probabilities. The thresholds were increased with increasing of the false alarm probability. The values of detection threshold corresponding to different false alarm probability are summarized in Table 2.
## 2 Synthetic Maritime Traffic Generation System (S-MTGS)  
S-MTGS consists of a maritime traffic generator and a collision avoidance algorithm. The maritime traffic generator generates virtual ship information via absolute and relative position methods.  
The absolute position method generates nearby ships based on a scenario that uses historical maritime traffic data from specific regions. The relative position method creates a virtual maritime traffic environment by generating nearby ships based on the encounter situation with the own ship, i.e., the encounter situation according to the Convention on the International Regulations for Preventing Collisions at Sea (COLREG), Distance to Closest Point of Approach (DCPA), Time to Closest Point of Approach (TCPA), and distance. The virtual ships generated based on the encounter situation with the own ship may perform collision avoidance with nearby ships, including the own ship, using the collision avoidance algorithm.  
The collision avoidance algorithm consists of ship detection, risk calculation, COLREG judgment, and avoidance action decision modules. It can initiate avoidance actions even when multiple ships are encountered by examining the encounter situations based on COLREG.
## 2 Related Works
### Extensible Messaging and Presence Protocol (XMPP)  
The Extensible Messaging and Presence Protocol (XMPP) [35] is a message-oriented protocol for streaming eXensible Markup Language (XML) elements with a real-time behavior. It is used combined with other very popular applications like Google App Engine [36]. Secure authentication (Simple Authentication and Security Layer or SASL) and encryption by means of Transport Layer Security (TLS) have been built into the core of XMPP specifications. XMPP runs over TCP and provides both Publish/Subscribe (asynchronous) and request/response (synchronous) messaging systems. It is designed for near real-time communications and thus, it supports small message footprint and low latency message exchange [37]. XMPP connects a client to a server using a stream of XML _stanzas_. An XML stanza represents a piece of code that is divided into three components: message, presence, and an iq pair (info/query). Message stanzas identify the source (from) and destination (to) addresses, types, and IDs of XMPP entities that retrieve data.  
XMPP has TLS/SSL security built in the core of the specification. However, it does not provide QoS options that make it impractical for M2M communications, although XMPP supports the Publish/Subscribe architecture that is more suitable for the IoT in contrast to CoAP request/response approach. Furthermore, it is an already established protocol that is supported all over the Internet as a plus when compared to the relatively new MQTT. However, XMPP uses eXtensible Markup Language (XML) messages that create additional overhead due to unnecessary tags and require XML parsing.
## 3 Research on the Influence of Track Structure Stiffness Change on the Stability of Vehicle
### Lateral Stiffness under Block  
According to the calculation results of the simulation conditions of different lateral stiffness under block, the maximum values of vehicle body lateral acceleration, vehicle body vertical acceleration, wheel-rail vertical force, wheel-rail lateral force, derailment coefficient, and wheel load reduction rate are summarized in Table 7, the changes are shown in Figure 9.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline Lateral stiffness under block (kN/mm) & 80 & 100 & 120 & 140 & 160 & 180 & 200 & 220 & 240 \\ Lateral acceleration (m/s\({}^{2}\)) & 1.534 & 1.451 & 1.404 & 1.42 & 1.381 & 1.437 & 1.528 & 1.534 & 1.451 \\ Vertical acceleration (m/s\({}^{2}\)) & 1.236 & 1.238 & 1.206 & 1.235 & 1.210 & 1.20 & 1.215 & 1.236 & 1.238 \\ wheel-rail vertical force (kN) & 185.8 & 186.1 & 184.9 & 185.3 & 184.4 & 185.2 & 185.3 & 185.5 & 185.0 \\ wheel-rail lateral force (kN) & 31.9 & 32.1 & 31.7 & 32.1 & 32.2 & 32.5 & 32.2 & 31.9 & 31.8 \\ Derailment coefficient & 0.188 & 0.183 & 0.193 & 0.198 & 0.193 & 0.200 & 0.192 & 0.187 & 0.192 \\ Wheel load reduction rate & 0.135 & 0.136 & 0.131 & 0.132 & 0.135 & 0.132 & 0.133 & 0.132 & 0.134 \\ Wear coefficient & 2.132 & 2.166 & 2.139 & 2.159 & 2.072 & 2.034 & 2.102 &Figure 9: _Cont_.  
According to the analysis in Table 7 and Figure 9, when the lateral support stiffness under block changes within the range of 80-240 kN/mm, the lateral and vertical acceleration of the train body changes in the range of 1.381-1.534 m/s\({}^{2}\), 1.206-1.238 m/s\({}^{2}\), the relative change rates are 11.08% and 2.65%, respectively, and the lateral and vertical acceleration of the vehicle body are both within the specified limits. The maximum value of wheel-rail vertical force, wheel-rail lateral force, derailment coefficient, wheel load reduction rate and wear coefficient vary with the stiffness of the lateral support under the block in 184.4-186.1 kN, 31.7-32.5 kN, 0.183-0.200, 0.131-0.136, 2.034-2.166, respectively. The rates of change were 0.93%, 2.36%, 9.15%, 3.91%, 6.51% and the amplitudes of various control coefficients for driving safety were all within the specification limits. When the stiffness of the lateral support under the block increases, the maximum amplitude of the wheel-rail vertical force, lateral force, and wheel load reduction rate does not change significantly.
## 3 System Architecture and Assumptions
### Network Architecture  
As shown in Figure 1, this paper considers UASNs that are constructed based on a cluster-based network. The sensor nodes in the network are assigned unique IDs. Furthermore, all underwater nodes possess identical initial energy levels, and their communication, computational, and storage capabilities are uniform and finite. It is assumed that the positional information of sensor nodes can be obtained through established localization algorithms [20]. Where the Sink node is deployed on the surface of the water, sensor nodes are randomly dispersed underwater, malicious nodes are hidden in the network, and the AUV cruises through the waters following a predefined path. The sensor nodes are divided into clusters based on a spectral clustering algorithm [21], and each cluster consists of a primary cluster head (CH), a supervisory cluster head (SCH), and several ordinary sensor nodes (SNs). The sensor nodes (SNs) are responsible for sensing environmental data in the marine surroundings and transmitting the data to the cluster head (CH) through either single-hop or multi-hop communication. The cluster head (CH) then consolidates the gathered data and transmits them to the nearby AUV, which transmits them to the Sink nodes, and data transmission between the Sink node and the Base Station is facilitated through satellite communication. In this setup, the supervisory cluster head (SCH) assumes the role of overseeing the actions of the cluster head (CH), with its conduct subject to monitoring by the ordinary sensor nodes (SNs).  
In the actual underwater environment, the position of sensor nodes is not static but moves with the currents. Changes in node locations affect the network links and the network topology, so it is necessary to introduce an ocean current model to simulate the realistic underwater environment. In this paper, the meandering current flow (MCM) model is introduced as an ocean current model [22]. In a shallow marine environment, the currents move mainly horizontally, and the vertical motion is negligible. The trajectory of a sensor node under complex ocean current motion is represented by the flow function \(\phi\). When the sensor node's initial position is \((x,y)\), the node's distance traveled at moment \(t\) is calculated as follows [23]:  
\[x=-\frac{\partial\phi(x,y,t)}{\partial y},\ y=\frac{\partial\phi(x,y,t)}{ \partial x}, \tag{1}\]  
where \(x,y\) denote the latitudinal and radial vectors of the moment \(t\) velocity field, with the unit of km.  
Figure 1: Network architecture.
## 4 Materials and Methods
### Bioassay with Insets
#### 4.3.2 Bioassays with Pyridine  
The bioassays were made using the olfactometry method with pyridine extracted from _Sarracenia alata_ plants. Pyridine was selected because of its possible effect in attracting insects, specifically fruit flies [39]. The pyridine (Sigma Aldrich, Saint Louis, MO, USA, cat. No. 1.09728) effect on insects was tested in three concentrations [\(v/v\)]: 1 \(\times\) 10\({}^{-3}\), 1 \(\times\) 10\({}^{-2}\) and 1, with hexane as the solvent. The tests were performed using a four-choice olfactometer (at the University of Science and Technology in Bydgoszcz, Poland) with the test area 55 \(\times\) 55 \(\times\) 5.5 cm as described by [PERSON] [48] in room conditions (22 \(\pm\) 2 \({}^{\circ}\)C). The air was removed from the center of the olfactometer by a vacuum pump, adjusted with a flow meter to 400 mL/min. The olfactometer arena was split into four areas: four by each way and additionally central area connected to pump for sucking air [49]. During the test one arm of the olfactometer was always assigned to the tested compound while three arms to the solvent (control). The tests were performed from the lowest to the highest concentration. Each odor chamber contained a filter paper disk (1.0 cm\({}^{2}\)) saturated with 50 \(\upmu\)L of a solvent or corresponding solution.  
The insects (the same species, as tested in bioassay) were introduced individually into the test area through the hole in the top of the olfactometer. Flies were able to make the choice within five minutes, while aphids were unable to effectively move in the test area. Therefore, only the response of flies was tested. During the test flies were introduced individually in 20 replications for each concentration. Each individual fly was exposed to a test concentration for seven minutes (two minutes to acclimatize in the olfactometer after which the experiment was run for further five minutes). The insects' behaviour in the olfactometer was observed and the time of their first reaction measured. The choice of each fly was recorded. Particular individuals were used only once in the test.  
The response was calculated as:  
\[R=\frac{\textit{No.}\textit{of individuals on the odour side}-\textit{No.}\textit{of individuals on the control side}}{\textit{Total No.}\textit{of individuals}} \tag{1}\]  
The data were analyzed in the SAS University Edition using the general linear model (GLM). The repeated factor was time (20 series for each concentration), while the area of the olfactometer (5 variants) was calculated as random effect.
# Resource Management in Cloud Radio Access Network: Conventional and New Approaches
## 2 Evolution of C-RAN Architecture, and Its Types, Advantages, and Challenges
### Advantages of C-RAN
#### 2.5.5 Interference Minimization  
In the traditional RAN, the movement of users from one cell to another, which alters the coverage area of the associated base stations, causes interference. This, in turn, results in decreased QoS. In the C-RAN architecture, many RRHs from different cell sites are connected to BBUs sharing the same BBU pool. User dynamicity does not cause interference as the change in coverage area of RRHs by users does. This is because BBUs from a large area are co-located to the same BBU pool, and the RRHs connected to those BBUs are also attached to that BBU pool. This mitigates the interference caused by user movement between different cell sites.
###### Abstract  
Connected Autonomous Vehicles (CAVs) have the potential to revolutionize traffic systems by autonomously handling complex maneuvers such as freeway ramp merging. However, the unpredictability of manual-driven vehicles (MDVs) poses a significant challenge. This study introduces a novel decision-making approach that incorporates the uncertainty of MDVs' driving styles, aiming to enhance merging efficiency and safety. By framing the CAV-MDV interaction as an incomplete information static game, we categorize MDVs' behaviors using a Gaussian Mixture Model-Support Vector Machine (GMM-SVM) method. The identified driving styles are then integrated into the flexible merging decision process, leveraging the concept of pure-strategy Nash equilibrium to determine optimal merging points and timing. A deep reinforcement learning algorithm is employed to refine CAVs' control decisions, ensuring efficient right-of-way acquisition. Simulations at both micro and macro levels validate the method's effectiveness, demonstrating improved merging success rates and overall traffic efficiency without compromising safety. The research contributes to the field by offering a sophisticated merging strategy that respects real-world driving behavior complexity, with potential for practical applications in urban traffic scenarios.  
connected autonomous vehicle; flexible merging decision; game; driving style +
Footnote †: journal: Applied sciences  
introduce complexity and uncertainty to the traffic environment. Accurately discerning the driving patterns of MDVs and gauging their cooperative intent is essential for CAVs to achieve safe and efficient merging. This study aims to explore the interaction between CAVs and MDVs during freeway merging. By analyzing driving conflict characteristics, modeling key issues, and proposing a tailored merge decision algorithm, this research holds significant theoretical and practical value for enhancing urban freeway traffic efficiency and ensuring road safety.
## 3 Mobile Node Design
### Node Networking  
The software environment for the sensor nodes is TinyOS, an open-source operating system designed for wireless embedded sensor networks [12]. The embedded software architecture of RacemoteZ is shown in Figure 4. A RacemoteZ communicates with other nodes or gateway by using a standard data format, namely _TOS_Msg_. The header of each TOS_Msg data packet that comes out of the radio module contains several data fields that are used to identify different network addresses, different network services and different network groups. The payload of TOS_Msg is interpreted as a command that will be sent to the mainboard to perform some actions, such as start, stop and move. If the node is performing a sensing task, the payload will be the ADC counts of the sensors. A RacemoteZ application receives command messages from the radio and interprets them. It will also forward command messages that it receives to other nodes in the network if necessary. This is accomplished by re-broadcasting the command message once it has been processed. In this way, a so-called _mesh network_ is formed by following a multi-hop routing protocol.  
Figure 4: The embedded software modules of the mobile node.  
Figure 3: A prototype of the mobile node.