esa-sceva/satcom-chunk-collection huggingface dataset:
# First Request First Service Entanglement Routing Scheme for Quantum Networks
## 5 Evaluation Results
### Evaluation with Different Sequences of Requests from Users  
The FRFS scheme can also dynamically allocate the resource to users who request the resource in different orders. Based on the network of case 4 in Table 2, two different require orders for 6 pairs of users were chosen to evaluate our scheme. The detailed orders are shown in Table 4. At time \(t_{1}\) of request order 1, users A and B request resources, and the FRFS scheme first searches the path from S to the former (A), and then searches the path from S to the latter (B), and the rest can be done in the same manner. The former (latter) of each pair of users is assigned to CH28-CH33 (CH40-CH35) in time order.  
The total loss between A and B under different orders of requests is shown in Figure 6. The paths assigned to the same pair of users differ when users access the network in different order of requests. This is due to the fact that the FRFS scheme locked the links after the users request the resource.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Request Sequence** & \(\mathbf{t_{1}}\) & \(\mathbf{t_{2}}\) & \(\mathbf{t_{3}}\) & \(\mathbf{t_{4}}\) & \(\mathbf{t_{5}}\) & \(\mathbf{t_{6}}\) \\ \hline
1 & AB & AC & AD & CD & BD & BC \\
2 & AC & AB & CD & AD & BC & BD \\ \hline \hline \end{tabular}
\end{table}
Table 4: The request sequences for 6 pairs of users. \(t_{i}\) means the request time and \(t_{i}>t_{i-1}\).  
Figure 6: The total link loss between A and B under different orders of requests. RS 1 (2) means the request sequence 1 (2) in Table 2.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Case** & **Path** & \(\mathbf{l_{4}}\) **(dB)** & \(\mathbf{l_{8}}\) **(dB)** & \(\mathbf{CC^{m}}\) **(kcps)** & \(\mathbf{R^{s}}\) **(cps)** \\ \hline
1 & \(AR_{2}R_{1}S_{1}R_{4}R_{3}B\) & 11.6 & 25.8 & 20.82 & 332.99 \\
2 & \(AR_{2}R_{3}S_{1}R_{4}R_{3}B\) & 11.1 & 27.5 & 15.80 & 251.85 \\
3 & \(AR_{2}R_{1}S_{1}R_{2}R_{3}B\) & 15.6 & 31.2 & 2.39 & 37.63 \\
4 & \(AR_{2}R_{1}S_{1}R_{4}R_{3}B\) & 15.6 & 25.3 & 9.30 & 148.79 \\
5 & \(AR_{2}R_{3}R_{1}S_{1}R_{4}R_{3}B\) & 28.2 & 22.3 & 1.02 & 16.19 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Paths found by the FRFS routing scheme. \(l_{A}\) (\(l_{B}\)) is the link loss from the entangled photon source to user A (B). \(loss_{u}\) is taken into account when calculating CC\({}^{m}\) and \(R^{s}\). \(AR_{2}R_{1}SR_{1}R_{3}B\) means the path \(A\gets R_{2}\gets R_{1}\gets S\to R_{1}\to R_{3}\to B\).
# Intelligent Transportation System Technologies, Challenges and Security
## 2 Intelligent Transportation Systems
### Intelligent Transportation Communication Technologies
#### 2.2.7 Cellular Communication Networks  
ITS, 3G, 4G, 4.5G, 5G, and future communication technologies are used for communication purposes such as mobile communication technologies, security, navigation, in-vehicle information systems, and entertainment services [28].
# Hardware-in-the-Loop Test of a Prosthetic Foot
## 2 State-of-the-Art
### Fundamentals of Hardware-in-the-Loop  
In Hardware-in-the-Loop (HiL), critical components are tested experimentally (experimental part) and coupled in real time with a simulation of the surrounding mechanical system (numerical part). Due to the coupling, the critical components are tested with realistic boundary impedance, and the loading of the components in the test is the same as it will be in real operation. In this work, mechanical-level HiL (denoted as HiL) was employed, which is also called real-time hybrid substructuring/simulation in the literature [13]. In mechanical-level HiL, the dynamical interaction between components of a system is analyzed by the exchange of force and displacement/velocity information. The coupling is performed by the transfer system, which comprises a controlled actuator, a Force-Torque Sensor (FTS), and a Digital Signal Processor (DSP).  
The standard signal flow to couple the numerical and experimental parts is as follows; cf. Figure 2. The explanations are given for the special case of one numerical and one experimental part, as this is the case in this work.  
The numerical model solves for the interface Degrees of Freedom (DoFs) (Vectors are indicated by variables written in bold. Variables printed in normal type are scalars.) \(\mathbf{z}\) using a numerical time integration scheme, typically with a fixed sampling time of \(\Delta\widehat{T}\). The actuator performs the position command and excites the experimentally tested component by the achieved motion \(\mathbf{z^{\prime}}\). Due to this loading, interface forces \(\mathbf{F}_{\text{int}}\) build and can be measured by the FTS. The block Force Adaptation denotes any filtering of the measured signals or transformations of the coordinate system. The measured interface forces are used for the next time integration step. The numerical, as well as the experimental part can be excited by external forces, which are denoted by \(F_{\text{ext}}^{\text{NUM}}\) and \(F_{\text{ext}}^{\text{EXP}}\), respectively. The exchange of displacement and force information is performed at a fixed sampling time, which is the synchronization time step \(\Delta T\).  
Figure 2: To couple the numerical (in blue) and experimental part (in green), a transfer system (in orange) is used. The transfer system consists of a controlled actuator, an FTS, and a DSP. The DSP is not visualized in the figure.  
HiL testing of foot prostheses has not been reported so far, and the purpose of this work was to set the foundation for such tests. This application of HiL is particularly challenging because of the transition from noncontact to contact, requiring a very robust control of the transfer system.
## 4 Discussion  
A control and measurement system for a Xenon arc lamp solar simulator was developed. They allow the user to monitor any device under test, as well as regulate the operating conditions of the solar simulator.  
The irradiance of the solar simulator at startup, and the corresponding lamp power, is shown in Figure 4. The lamp power over 180 s shows some stability, when compared to the irradiance over the same time frame. The PLC tries to maintain and keep the irradiance of the solar simulator constant, although slight fluctuations in irradiance can be noticed in Figure 7. The gradual ramping down and up of the solar simulator's input power, as the PLC tries to keep it constant, has a direct impact on the irradiance of the solar simulator. It can be seen in Figure 7 that the lamp power became stable after 120 s of starting the solar simulator. Although there was a slight fluctuation in the irradiance value after 120 s, it ramped up almost immediately thereafter, and remained relatively stable. This could have been a result of the slight change in the lamp's input power. The effect of the small fluctuations in the lamp's input power is seen in the percentage difference of the irradiance from the set point in Figure 4, which is less than \(\pm\)1%.  
To find out what input current would give 1000 Wm\({}^{-2}\), the solar simulator was run at 20%, 50% and 90%, respectively. Running the solar simulator at 50% gave an irradiance value of over 500 Wm\({}^{-2}\), as shown in Figure 5. The solar simulator was run for over 70 s. The irradiance remained relatively stable at 500 Wm\({}^{-2}\) range. The solar simulator was then operated at 90% of its rated current. The resulting irradiance value was 1000 Wm\({}^{-2}\), as shown in Figures 9, 11 and 12, as obtained by [26].  
The reference cell's surface temperature over 120 s is shown in Figures 9-11. According to [5], it takes pulsed solar simulators approximately 120 ms to carry out a test on PV cells. It took the reference cell between 35 s and 40 s to exceed 25 \({}^{\circ}\)C without temperature control. This was ample time to carry out the test on the PV cells after they had been mounted on the target area, as suggested by [5] for steady-state solar simulators. However, the total heat required to maintain 25 \({}^{\circ}\)C in the reference cell, while carrying out a test on the PV module, was determined as 1660 Joules.
## 3 Research Method
### Analysis of Problems in ERT
#### 3.1.2 Influence of the Boundary Effect  
In terms of the boundary effect, the 2D ERT measurement is performed on a straight line of finite length, but if there are topographical changes on two lateral boundaries of the measuring line, e.g., ponds, valleys, or substructures like concrete structures or metal pipelines, the resistivity may be mapped onto the 2D electrical resistivity profile, inducing detection errors, and influencing the measurement result and interpretation accuracy.
## 2 The Overall Architecture Design of the System  
The system adopts a modular design and consists of a field monitoring subsystem, a database subsystem and a remote monitoring subsystem (Figure 1).
## 4 IoT for Geospatial Research
### Data Sharing and Interoperability  
An interesting aspect is whether some authors have provisioned to use data types and approaches which promote content sharing and reuse. Only few research papers followed this path, such as COMPASS [20], employing semantic web technologies (i.e., OWL) for enhancing the tourism-related data, allowing easier data reuse and understanding. Semantic web technologies and the principles of Linked Open Data (LOD) were used also in [57], for facilitating collaborative analytics of cultural heritage data. The CityPulse project created convincing demonstrations in different smart city scenarios, involving semantically-annotated data [12] and analysis/reasoning based on semantic web technologies (i.e., OWL, RDF, and SPARQL).  
Moreover, the web-based geospatial toolkit for the monitoring of dengue fever [48] tried to promote data reuse by means of exposing data collected by using common data formats such as CSV and KML, while the air quality measuring platform presented in [46] selected JSON as a universal format for data exchange. Data were stored in a MySQL database in [66], and then made available any time and anywhere through the Internet.  
The work in [61] refers to a lack of compatibility between OpenStreetMaps and Google's Map Maker, which made crowdsourced content during the Haitian earthquake difficult to understand.  
Interoperability among IoT-based services, either at platform- or device-level is a relevant topic here too. From the few cases recorded, COMPASS [20] used WS-* web services for interacting with tourism-related services and getting relevant data. Further, the concepts of the _Web of Things_[76] were followed in [13], for deploying RESTful API for interacting with real-world services dealing with geo-located data, being able to easily combine these services towards creating _urban mashups_[14]. Finally, the air quality platform in [46] developed a plug-and-play architecture for easier adaptation to new software modules and data formats, using web services for interacting with the platform. The rest surveyed papers did not reveal any information about data sharing or device/service interoperability.
# Assessment of Limb Imbalance in Professional Soccer Players
## 4 Discussion
### Tests Measuring Dynamic Balance  
The dynamic balance represents the ability to perform an action while maintaining or restoring a stable position and plays an important role in soccer, as it requires strength, adequate range of motion, proprioception, and neuromuscular control to complete actions such as kicking, passing, dribbling and defending [43]. Therefore, a dynamic balance test should be performed in order to identify soccer players at risk of lower limb musculoskeletal injuries [44,45].  
In the present study, two papers were found [20,28] that tried to assess limb imbalance through dynamic balance tests. To this end, [PERSON] et al. [20] used the YBT to assess limb imbalance in the anterior, posteromedial, and posterolateral planes, taking into account the position of each player on the field of play. In this study [20], there were no significant differences in the anterior, posteromedial, and posterolateral scores among the positions of each player's group. The YBT has been widely used to identify potential risks of injury in team sports by other researchers [46], demonstrating its reliability and efficacy in a rigorous manner. In addition, the YBT allows discriminating athletes at risk of suffering a lower limb injury due to limb imbalance since it requires balance, lower limb strength, and flexibility [47]. Therefore, we can determine that the YBT would be useful to detect the risk of injury in professional soccer players, but we cannot consider it a valid test to measure limb imbalance among positions in professional soccer because there is no study that makes this distinction. On the other hand, [PERSON] et al. [28] used the mSEBT, verifying that this test showed acceptable sensitivity in detecting limb imbalance in professional soccer players. However, since the study was carried out only on athletes with no previous history of injuries and in a single period of the season, future studies are needed to expand information related to the mSEBT. Furthermore, it is important to bear in mind that the tests analyzed in the present study focused on healthy adult professional soccer players. In this respect, it is necessary to clarify the potential of both dynamic balance tests to provide information that will help coaches and trainers prevent injuries resulting from limb imbalance.  
In summary, the YBT appears to be a useful tool for assessing injury risk in professional soccer players. However, it may not be sensitive or valid enough to detect differences between playing positions. Future research should focus on confirming its utility in evaluating limb imbalances in professional soccer players, which is in line with the purpose of assessing test measures for asymmetry.
# Smart Product Design Process through the Implementation of a Fuzzy Kano-AHP-DEMATEL-QFD Approach
## 3 Proposed methodology
### Dematel  
The procedure of DEMATEL method can be described by the following four steps:  
1. _Calculate the average matrix_: Each respondent is asked to evaluate the direct influence between any two factors by an integer score ranging from 0, 1, 2, and 3, representing 'no influence', 'low influence','medium influence', and 'high influence', respectively. The notation of \(x_{ij}\) represents the degree to which the respondent believes factor \(i\) affects factor \(j\). For \(i=j\), the diagonal elements are set to zero. For each respondent, an n x n non-negative matrix can be established as \(X^{\prime}k\) = \(\left[x_{ij}\right]^{k}\), where \(k\) is the number of respondents, where \(1\leq k\leq H\), and \(n\) is the number of factors. So, \(X^{\prime}1\), \(X^{\prime}2\), \(X^{\prime}3\), \(\ldots,\)\(X^{\prime}H\) are the matrices from \(H\) respondents. To summarize all opinions from \(H\) respondents, the average matrix \(A=\left[a_{ij}\right]\) is constructed as follows (refer to Equation (8)): \[a_{ij}\frac{1}{H}\underset{k=1}{\overset{H}{\sum}}x_{ij}^{k}\] (8)
2. _Compute the normalized initial direct-relation matrix_, where normalization of initial direct-relation matrix D is performed by \(D=A\times S\), where \(S\) is calculated by implementing Equation (9). \[S=\ \frac{1}{\underset{1\leq i\leq n}{\overset{H}{\underset{j=1}{\underset{j=1}{Each element in matrix \(D\) ranges between zero and one.
3. _Calculate the total relation matrix \(T\),_ where \(T\) is defined as stated in Equation (10). \[T=D\ (I-D)^{-1}\] (10) Here, \(I\) is the identity matrix.  
Calculate the sum of the rows (\(r_{i}\)) and the sum of the columns (\(c_{j}\)) of the total relation matrix \(T\). Suppose that \(r_{i}\) is the sum of the \(i\)-th row in matrix \(T\), then \(r_{i}\) summarizes both direct and indirect effects given by factor \(i\) to the other factors. If \(c_{j}\) denotes the sum of the \(j\)-th column in matrix \(T\), \(c_{j}\) shows both direct and indirect effects by factor j from the other factors. When \(j=i\), the sum (\(r_{i}+c_{j}\)) illustrates the total effects given and received by factor \(i\). Thus, (\(r_{i}+c_{j}\)) represents the importance degree of factor \(i\) in the entire system. On the other hand, the difference (\(r_{i}-c_{j}\)) indicates the net effect that factor \(i\) contributes to the system. Moreover, if (\(r_{i}-c_{j}\)) is positive, factor \(i\) is a net cause, while factor \(i\) is a net receiver or result if (\(r_{i}-c_{j}\)) is negative [65].
4. _Set up a threshold value to obtain the digraph_: Since matrix \(T\) provides information on how one factor affects another, a decision-maker must set up a threshold value to filter out some negligible effects. Only the effects greater than the threshold value are chosen and depicted in a digraph. The threshold value is usually set up by computing the average of the elements in Matrix \(T\). The digraph can be acquired by mapping the dataset described in step 3 [65].
# Pathophysiologyiology and Treatment of Soriasis: From Clinical Practice to Basic Research
## 3 Pathogenesis of Psoriasis
### Cell Signaling Pathways Related to Soriasis
#### 3.3.4 JAK (Janus Kinase)/STAT (Signal Transducer and Activator of Transcription) Signaling Pathway  
The JAK family, including JAK1, JAK2, and TYK2, activates STAT3, driving Th17 cell activation and cytokine production [47]. STAT3 is involved in regulating cell growth and apoptosis and plays an essential role in the differentiation of Th17 cells (a T cell subset that secretes IL-17) [48]. When the skin is exposed to external stimuli, dendritic cells and monocytes in the dermis release IL-23 to induce Th17 cells to produce large amounts of cytokines to activate STAT3 in keratinocytes (KCs), which will drive epidermal  
Figure 1: Triggers and pathogenesis of psoriasis. After IL-23/Th17 axis is activated, a large number of inflammatory factors are produced, which are further exacerbated by the CCL20-CCR6 pathway, e.g., IL, TNF, IFN, etc., which bind to receptors on the cell membrane and further transduce inflammatory signals inside the cell, exacerbating the symptoms of psoriasis. Created with BioRender.com.  
hyperproliferation, promote leukocyte recruitment, and facilitate the formation of mature psoriatic plaques [49].  
Tyrosine kinase 2 (TYK2), a key regulatory molecule, has emerged as a promising therapeutic target due to its pivotal role in mediating pro-inflammatory and immune-stimulatory signaling driven by cytokines such as IL-12, IL-23/IL-17, and type I interferons [50]. To avoid affecting other JAK family members, researchers are focusing on TYK2's regulatory domain. By targeting this domain with small-molecule ligands, it is possible to selectively inhibit TYK2 signaling without interfering with other JAK pathways [51].
## 3 Results
### Comparison with Mainstream Networks  
In order to verify the superiority of YOLOv5s-x in terms of accuracy, number of parameters, and model size, six different target detection models were trained and tested on the dataset, namely YOLOv5s, YOLOv5s-x, YOLOv7, YOLOv8n, YOLOv8s, DETR [29].  
Figure 7: Comparison chart of detection effect. (**A–D**): prediction results of YOLOv5s; (**E–H**): Prediction results of YOLOv5s-x.  
The experimental results are shown in Table 1, and the relationship between the mAP of each model and parameters, GFLOPs, and model size for each model are shown in Figure 8. This study used mAP as an evaluation metric for the accuracy of detection and classification tasks and used the number of parameters, GFLOPs, and model size as evaluation metrics for model complexity.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **mAP** & **Parameters** & **GFLOPs (G)** & **Model Size (MB)** \\ \hline YOLOv5s & 0.896 & 7,067,395 & 16.3 & 13.8 \\ YOLOv5s-x & **0.925** & 7,026,307 & 15.8 & 13.7 \\ YOLOv7 & 0.920 & 37,221,635 & 105.2 & 71.3 \\ YOLOv8n & 0.913 & **3,006,818** & **8.1** & **6.2** \\ YOLOv8s & 0.918 & 11,127,906 & 28.4 & 22.5 \\ DETR & 0.874 & 36,724,684 & 101.3 & 474 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Experimental results of six networks.  
Figure 8: Graph of the relationship between mAP and other indicators. (**a**) Diagram between mAP and parameters; (**b**) diagram of the relationship between mAP and GFLOPs; (**c**) diagram of the relationship between mAP and model size.  
According to Table 1, it can be seen that among the six different target detection models, YOLOv5s-x has the highest mAP of 0.925, surpassing the newly introduced YOLOv8n and YOLOv8s. The mAP of YOLOv7 is closest to that of YOLOv5s-x, but the number of parameters of YOLOv7 is 5.3 times higher than that of YOLOv5s-x. YOLOv7 has 6.7 times the GFLOPs of YOLOv5s-x, and the model size of YOLOv7 is 5.2 times that of YOLOv5s-x. YOLOv8n has the smallest number of parameters and GFLOPs, with 0.43 times the number of parameters, 0.51 times the GFLOPs of YOLOv5s-x, and a model size of YOLOv5s-x by a factor of 0.45, but the mAP is 1.2% lower than YOLOv5s-x.  
Although YOLOv5s-x is not optimal in terms of a number of parameters, GFLOPs, and model size, it has a much lower number of parameters, GFLOPs, and model size than most models, while YOLOv5s-x has the best mAP performance of all models. Therefore, YOLOv5s-x is easier to deploy and achieves better detection and classification results in practical applications. These results suggest that it is reasonable to use YOLOv5s as the baseline model and perform knowledge distillation and that the YOLOv5s-x model is more suitable for the detection and classification of the impacted mandibular third molar.
# A Systematic Literature Review of Reliable Provisioning for Virtual Network Function Chaining
## 5 Discussion
### Protection Scheme or Recovery Plans  
The main difference between the reliable provisioning algorithms is their approach to providing a reliable algorithm. Some algorithms rely on avoiding failure by proposing a protection scheme [18; 37; 38] to increase the service availability, while others focus on recovery plans to deliver the service at the earliest point without increasing the resource usage and overhead for the network [10; 39]. In protection schemes, backup VNFs or links are considered to be active when a failure occurs, increasing resource usage. On the other hand, recovery plans are proposed to be used when a failure happens without considering extra resources. In comparison to protection schemes, they create a balance between the resource usage and service availability. Therefore, where service availability is crucial, protection schemes could be a better solution, and, in scenarios where resource usage is important, recovery plans could be used. Although there are many existing algorithms with each of these approaches, there is limited research on recovery plans, especially where only the failed link or VNF will be re-routed without re-routing the whole SFC. Moreover, the solutions that consider both protection and recovery at the same time to increase service availability and reduce resource usage at the same time are insignificant. Hence, future studies can focus on reducing resource usage while providing higher service availability.  
Figure 4: Occurrence of failure in provisioning algorithms.
# Single-Baseline RTK Positioning Using Dual-Frequency GNSS Receivers Inside Smartphones
###### Abstract
When using a short baseline configuration (less than few kilometers), it is possible to assume that common spatial-related biases affect both devices; thus, the goal is to show the positioning performances of a smartphone in case of static surveys with short baselines, if the master station is a smartphone as the rover station.