esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Open Challenges, Future Prospects and Research Directions
### ITU Recommendations  
It is important to note that the International Telecommunication Union (ITU) has recognized the potential of FSO systems, and through the ITU--Telecommunications Standardization Sector (ITU-T), it has approved Recommendation G.640 [498], providing a procedure for enabling the interference-free coexistence of more than one p2p FSO communication system at a location. The Radio Communication Sector of ITU (ITU-R) has also issued a report on fixed service use cases using FSO links, defining device characteristics, possible applications, as well as technical and operational aspects of FSO systems [499]. Also, ITU has developed recommendations that cover aspects of optical communication, including FSO. ITU-T G.9991, for instance, deals with high-speed indoor OWC transceivers, which can inform FSO developments [500; 501; 502]. ITU-R P.1817 provides guidelines for the performance and availability of optical free-space links, addressing atmospheric effects and link budgets. These recommendations are critical for ensuring reliable FSO communication across different environments [503; 504]. The recommendation in [505] provides propagation prediction methods for planning terrestrial FSO systems. It includes methods to estimate attenuation in clear air, fog, and rain and snow precipitation. It also covers scintillation and impairments by sunlight.
# Diag-Skills: A Diagnosis System Using Belief Functions and Semantic Models in ITS
## 4 Diag-Skills: The Diagnosis Model
### The Diagnosis Process
#### 4.2.1 The Transformation Function  
Diag-Skills' reasoning is based on mass distributions. It uses these masses in order to determine the state of the learner's skills. However, as indicated in the architecture, the diagnosis module receives numerical grades from the evaluation module. These numerical values are not directly processable by Diag-Skills and need to be transformed into a distribution of belief masses. To do so, we define the transformation function \(\mathrm{T}(ne,E,\ \epsilon)\). The first parameter (\(ne\)) refers to the score obtained by the learner in a given assessment. It is defined in the range [0, \(n\)], where 0 and \(n\) represent respectively the minimum and maximum score that the learner can get in an assessment. The second parameter \((E)\) represents the minimum score required to validate the assessment. Finally, \((\epsilon)\) represents a range of doubt on \(E\). Based on these parameters, this function returns distribution of belief masses (i.e., the values of \(m(a),\ m(\
eg a),\ m(i)\), and \(m(c)\)) such that \(m(a)+m(\
eg a)+m(i)+m(c)=1\). The function is illustrated in Figure 4.  
Figure 4: The transformation function.  
Figure 3: The diagnosis process.  
The belief masses are defined as follows:  
\[m(\
eg a)=\left\{\begin{aligned} -\frac{1}{E+\varepsilon}\ x+1,\ 0\leq x<E+ \varepsilon\\ 0,\ E+\varepsilon\leq x\end{aligned}\right., \tag{2}\]  
\[m(a)=\left\{\begin{aligned} \frac{1}{n-E+\varepsilon}\ x+\left( \begin{aligned} -E+\varepsilon\\ n-E+\varepsilon\end{aligned}\right),\ E-\varepsilon<x\leq n\\ 0,\ x\ \leq\ E-\varepsilon\end{aligned}\right., \tag{3}\]  
\[m(i)=\left\{\begin{aligned} 1-m(\
eg a)\ 0\leq\ x<E-\varepsilon\\ 1-m(\
eg a)-m(a)\ E-\varepsilon\leq\ x\leq E+\varepsilon\\ 1-m(a)\ E+\varepsilon<x\end{aligned}\right. \tag{4}\]  
Let us take an example of a learner who got a score of 14 out of 20 on a test in which the required score to validate it is 10, with a doubt of 2 out of 20. The belief mass distribution is in this case:  
\[m(a)=0,\ m(\
eg a)=0.5,\ m(i)=0.5,\ m(c)=0.\]
## 3 Results
### Cellular Functions  
Glucose consumption by Hep G2 cells was determined and standardized by cell number in 10 day rotary cultures. As shown in Figure 6a, there was an irregular change on Day 3 that was probably caused by prolonged observation outside the incubator. The highest cellular glucose consumption per immobilized cell was observed in the transverse-pore module, and was found to be approximately 1.1 times higher than that in the cells in the cylindrical modules, and 1.2 times higher than that in the cells in the Raschig ring modules on Day 10. Albumin production by Hep G2 cells was also determined and standardized by cell number. As shown in Figure 6b, the detected albumin amount decreased steeply on Day 2 concurrently with decreased glucose consumption, but gradually increased during the remaining culture period. The highest albumin production in the 10 day culture was achieved by cells cultured in the transverse-pore modules, approximately being 1.6 times higher than that in cells in the cylindrical modules, and 1.2 times higher than in cells in the Raschig ring modules on Day 10, reinforcing the advantage of this module aside from the glucose-consumption aspect.  
Figure 5: Confocal images of (**a**) PKH-labeled Hep G2 cells and (**b**) calcein AM–stained live cells on day 10 of culturing.  
Significant differences in cellular function were observed between the cylindrical, Raschig ring, and transverse-pore modules. In line with the number of immobilized cells on Day 10, immobilized cells in the transverse-pore module showed better cell function and higher viability than those in other models, which was also reinforced by the qualitative results from the viability assay through images obtained by confocal microscopy. Given the fact that albumin secretion is sensitive to oxygen concentration, this result indicated that the provided hollow structures improved mass transfer [33].
## 2 Methodology  
### Overall Procedure  
The overall procedure of this study is outlined in Figure 2, which provides a step-by-step framework for evaluating community-level flood resilience. The process begins by identifying flooding hazard features that effectively represent the level of damage caused by flooding. These features are critical for characterizing the severity and scope of flood events and serve as the foundation for subsequent analyses. The next step involves analyzing the vulnerability of various infrastructure types to flooding. This is depicted in the second panel of Figure 2, which presents fragility curves for different infrastructure categories, including residential, commercial, and industrial buildings. Each curve illustrates how the probability of reaching specific damage states increases with flood depth. This analysis is essential for identifying which structures are most at risk and determining the thresholds of flood intensity that pose the greatest concern. Following the fragility curve analysis, the survivability of individual buildings is evaluated across a range of increasing flood depths. The third panel in Figure 2 displays a cumulative distribution function that quantifies the likelihood of buildings maintaining functionality as flood intensity escalates. This step provides valuable insights into structural vulnerabilities and supports the development of targeted strategies, such as structural reinforcements and other mitigation measures, to enhance building resilience. In the fourth step, the focus shifts to assessing the interdependencies among critical community sectors, including water, power, and transportation systems. The interdependency matrix shown in the fourth panel quantifies how failures in one sector can propagate and impact others. This analysis highlights key areas where joint resilience strategies could effectively reduce the cascading effects of sectoral failures, ensuring a more robust response to flooding events. Finally, the data from all preceding steps are synthesized to evaluate the overall functionality of the community in the face of floods. The fifth panel in Figure 2 presents a cumulative distribution function that illustrates the probability of maintaining community-wide functionality as flood depths increase. This comprehensive assessment provides a macroscopic perspective on the broader impacts of floods, enabling strategic planning for disaster preparedness, response, and recovery. By integrating hazard characterization, infrastructure vulnerability analysis, interdependency assessment, and community-wide functionality evaluation, this methodology offers a systematic approach to understanding and enhancing flood resilience at the community level.
## 3 Proposed System Model
### Task Offloading Model  
Task offloading on the edge servers refers to the process of transferring computational tasks from the user device to more powerful servers located at the edge of the network. The transmission time of task \(i\) from user device is given in (5), where \(D_{i}\) is the data size of computation task \(i\) to be distributed to the edge server for computation and \(t^{off}\) is the offloading time of task \(i\) from mobile device to the edge server.  
\[t^{off}=\frac{D_{i}}{R_{nj}} \tag{5}\]Energy offloading to the edge server means the energy for offloading computing tasks from a user device to an edge server, where \(e^{off}\) is the energy for offloading task from task-\(i\) to the edge server and \(p_{m}^{tr}\) is the fixed transmission power of mobile device \(n\) for offloading task \(i\) to the edge server. Computation cost for offloading task \(i\) to the edge server can be expressed as (6), where \(cost^{off}\) is the cost of energy and latency for offloading task \(i\).
# On Ghost Imaging Studies for Information Optical Imaging
## 3 Ghost Imaging Studies for Information Optical Imaging
### Task-Oriented Gi System Design  
In many practical scenarios, a high-quality image is not necessarily required; instead, it is sufficient to only have the information related to specific tasks [24; 107]. Intuitively, this can be realized by designing the imaging system with the help of information theory. Traditional imaging systems, however, usually need to first perform high-quality imaging and then perform a specific analysis based on a sensing-storing-computing-integrated equipment due to their fixed image information transmission mode. Since the GI system can perform more flexible encoding than traditional imaging systems, it is rather suitable for such a task-oriented kind of application scenario so that the desired information can be acquired and retrieved without the need for performing high-quality imaging. Currently, several task-oriented GI studies have been performed, mainly realized by designing the encoding light-field patterns according to the desired task information and assisting with data-processing methods. They can be classified into several categories, including non-imaging object detection [108], non-imaging object classification [109; 110], non-imaging object edge detection [111; 112], and object tracking [113] as well as progressive imaging [114]. In brief, these studies largely utilize the flexibility of the information mapping mode of GI systems. However, they are mostly come up with from a heuristic perspective rather than a more theoretically solid one. Thus, for further task-oriented studies, GI systems may be combined with the task-specific information measure [24] to develop a more complete information-theoretic framework for designing systems to perform a specific task. Task-oriented GI systems should significantly reduce the excessive demand of the transmission and storage of redundant information, thereby saving much of the energy consumption of devices and leading to a more \"green\" task information acquisition mode for many application scenarios, such as automatic driving and the industrial internet of things.
## 2 Methodology
### Power Generation Performance Loss Factors
#### 2.2.1 Module Surface Temperature  
Previous research [26] showed that the annual power generation loss of a PV system due to an increase in module temperature is up to 17%. If the installation type of a PV array allows enough rear ventilation and the cooling effect by ambient air, and airflow is large, the losses due to a temperature rise can be reduced. On the other hand, rear ventilation conditions are not easily formed because the BIPV module in system is used as a building material to replace the envelope and windows of buildings. Therefore, the temperature loss factor is a power generation performance loss factor that requires extra consideration regarding the BIPV system. To analyze the performance loss due to an increase in the module temperature, this study compared the power generation performance before and after a temperature loss correction using the DC power, the module temperature, the temperature coefficient, and the equation for the temperature-corrected DC power. Equation (7) [25] was used to obtain the temperature-corrected DC power:  
\[E_{AT}=E_{A}/1+\alpha_{AT}\cdot(T_{C}-25) \tag{7}\]  
where \(E_{AT}\) is the temperature-corrected theoretical DC energy generation (kWh), \(E_{A}\) is the measured DC energy generation (kWh), \(\alpha_{AT}\) is the temperature coefficient (%/K), and \(T_{C}\) is the module temperature (K).
## 2 Basic Concepts of DQ0-Based Models
### Modeling General Linear Networks
, \tag{39}\]  
and choosing the reference frame such that \(\frac{\mathrm{d}}{\mathrm{d}t}\theta=\omega_{s}\) leads to  
\[\frac{\mathrm{d}}{\mathrm{d}t} \begin{bmatrix}i_{d}\\ i_{q}\\ i_{0}\end{bmatrix} = \begin{bmatrix}-\frac{R}{L}&\omega_{s}&0\\ -\omega_{s}&-\frac{R}{L}&0\\ 0&0&-\frac{R}{L}\end{bmatrix}\begin{bmatrix}i_{d}\\ i_{q}\\ i_{0}\end{bmatrix} +\frac{1}{L} \begin{bmatrix}v_{d,1}-v_{d,2}\\ v_{q,1}-v_{q,2}\\ v_{0,1}-v_{0,2}\end{bmatrix} . \tag{40}\]  
The resulting model is linear and time-invariant. What happens if the network is not symmetrically configured? Assume now that the network is changed, such that the inductances are \(L\), \(L\), and \(2L\). In this case the equality \(\Lambda_{\theta}A=A\Lambda_{\theta}\) no longer holds, and the resulting model is nonlinear and time-varying, since the term \(\Lambda_{\theta}A\Lambda_{\theta}^{-1}\) changes with \(\theta(t)\). As another example, consider the circuit in Figure 6. The voltage sources on the left represent the secondary side of a transformer, and the sources on the right represent the primary side of another transformer. The objective is to study the effect of the resistors \(R_{g,1}\) and \(R_{g,2}\). Figure 5: Example—symmetrically configured \(RL\) transmission line. The model in the \(abc\) reference frame is  
\[L\frac{\mathrm{d}}{\mathrm{d}t}i_{a} =-Ri_{a}-(R_{g,1}+R_{g,2})(i_{a}+i_{b}+i_{c})+(v_{a,1}-v_{a,2}),\] \[L\frac{\mathrm{d}}{\mathrm{d}t}i_{b} =-Ri_{b}-(R_{g,1}+R_{g,2})(i_{a}+i_{b}+i_{c})+(v_{b,1}-v_{b,2}), \tag{41}\] \[L\frac{\mathrm{d}}{\mathrm{d}t}i_{c} =-Ri_{c}-(R_{g,1}+R_{g,2})(i_{a}+i_{b}+i_{c})+(v_{c,1}-v_{c,2}).\]  
Direct computations yield  
\[\Lambda_{\theta}A\Lambda_{\theta}^{-1}= \begin{bmatrix}-\frac{R}{L}&0&0\\ 0&-\frac{R}{L}&\\ 0&0&-\frac{R+3(R_{g,1}+R_{g,2})}{L}\end{bmatrix} . \tag{42}\]  
Assuming \(\mathrm{d}\theta/\mathrm{d}t=\omega_{s}\), and using (33), the \(dq0\) model becomes  
\[\frac{\mathrm{d}}{\mathrm{d}t}i_{d} =-\frac{R}{L}i_{d}+\omega_{s}i_{q}+\frac{1}{L}(v_{d,1}-v_{d,2}),\] \[\frac{\mathrm{d}}{\mathrm{d}t}i_{q} =-\omega_{s}i_{d}-\frac{R}{L}i_{q}+\frac{1}{L}(v_{q,1}-v_{q,2}), \tag{43}\] \[\frac{\mathrm{d}}{\mathrm{d}t}i_{0} =-\frac{1}{L}(R+3(R_{g,1}+R_{g,2}))i_{0}+\frac{1}{L}(v_{0,1}-v_{0,2}).\]  
Note that in this case \(\Lambda_{\theta}A\
eq A\Lambda_{\theta}\), nevertheless the resulting \(dq0\) model is linear and time-invariant. The expressions for \(i_{d}\), \(i_{q}\) are identical to the expressions in (40), and are unaffected by the mutual resistors \(R_{g,1}\) and \(R_{g,2}\). These resistors affect only the zero component, which according to (1) represents the average current \(i_{0}=\frac{1}{3}(i_{a}+i_{b}+i_{c})\). Specifically, if \(R_{g,1}+R_{g,2}\rightarrow\infty\), the average current \(i_{0}\) tends to zero, but \(i_{d}\), \(i_{q}\) are unaffected. This example may explain how a transformer with isolation between the primary and secondary coils helps to balance the system, by attenuating the current zero component.
## 1 Introduction
The limitation of the upper frequency range is due to a significant decrease in the light output coefficient \(\eta\) of LEDs at frequencies above 100 kHz [12]. In the range of 0.3-100 kHz, the luminous flux of LEDs (for example, LED-003W-13C-220-240 LM-B-PS manufactured by Bridgelux, USA) at a constant current remains practically unchanged and depends only on the fill factor [13]. We analyzed the dependence of LEDs' luminous efficiency on the pulse frequency using different values for the pulse filling factor D given in [3]. Our conclusion was that when choosing the operating frequency of the LED device driver, it is necessary to consider the value of the LED's luminous efficiency and the efficiency of its output stages. Therefore, the optimal driver frequencies are in the range of 20-30 kHz, that is, outside the range of sound frequencies, in which the LED light output is maximum. Also, the fill factor D of the driver output pulses should be no less than 0.5 because LEDs' luminous flux and light output are more significant in this case. The objectives of this research are as follows:  
1. To determine the effect of ambient temperature on the electrical and luminous parameters of LED lighting devices powered by pulsed voltage in the form of rectangular pulses;
2. To investigate how the frequency of the voltage (current) pulses that supply LEDs affects their luminous flux at different ambient temperatures;To investigate the effect of ambient temperature on the electrical parameters (voltage, current) and luminous flux of LEDs when they are powered by pulsed voltage with different pulse filling factors;  
To compare the temperature dependence of the electrical parameters and luminous flux of LEDs when a constant voltage source and a pulsed source power them;  
To propose a constructive solution for an LED driver powered by pulsed voltage and to study the effect of its temperature regime on the electrical parameters and luminous flux of the LED.
## 7 Conclusions and Future Work  
In this paper, a novel joint spatial-code clustered IA scheme has been proposed to mitigate the strong interference that cannot be suppressed well by the traditional IA system in large-scale wireless sensor networks with limited antennas. In the proposed JSCC-IA scheme, the users are clustered into several groups, and traditional IA methods such as the Max-SINR algorithm are applied within each group to eliminate the intra-group interference. Moreover, each group is assigned with an exclusive PN code so than the inter-group interference can be mitigated through the orthogonality of the PN codes. The analytical BER expressions of the the proposed JSCC-IA scheme have been formulated in the situations of identical and different propagation delays, respectively. Both of the symmetric and asymmetric networks have been considered, and a random selection grouping algorithm has been introduced to search for better grouping combinations for the latter circumstance. Numerical results have demonstrated that the proposed JSCC-IA scheme outperforms the conventional IA system, and can achieve significantly lower BER and higher received SINR in the situation where the number of users exceeds the IA feasibility constraint.  
Perfect CSI is assumed to be available and the signaling feedback strategy is not discussed in this paper. However, in the practical WSNs, the ideal CSI might be difficult to acquire due to the feedback latency, estimation error and so on. In our future work, we will extend our work to the scenario of  
Figure 16: The average received SINR of the conventional IA scheme, the JSCC-IA schemes with identical/different propagation delays and the RGS JSCC-IA schemes with identical/different propagation delays in the (6 \(\times\) 6, 2)\({}^{18}\) asymmetric networks.  
imperfect CSI to make the proposed scheme more practical. Moreover, we will further investigate the relationship between the grouping combinations and the error performance. We will develop a more intelligent and efficient clustering algorithm to search for even better grouping combinations.
# Evaluation of the Effects of Hidden Node Problems in IEEE 802.15.7 Uplink Performance
## 2 Fundamentals of Optical Wireless Communications  
Optical wireless links for indoor environments can be classified according to two criteria. The first criterion is the degree of directionality of the optical transmitter and receiver. The second one relates to whether the link relies upon the existence of an uninterrupted line of sight (LOS) path between the transmitter and receiver [14].  
* Directed links resort to directional transmitters and receivers, which must be aimed in order to establish a link. Directed links maximize power efficiency since path loss and reception of ambient light noise is minimized. They have the disadvantage of limiting mobility in user terminals because they need to be aligned. On the other hand, non directed links may be more convenient to use for mobile terminals, since they do not require precise alignment. However, non directed links present some disadvantages--such as multipath distortion and lower power efficiency--that make it difficult to achieve high transmission rates.
* LOS links require a direct path between the transmitter and receiver that is without obstacles. They allow for maximizing received power and minimizing multipath distortion, but the need for a line of sight diminishes flexibility of use. Non-LOS (NLOS) links are more robust because they can operate even in the presence of obstacles between the transmitter and receiver. NLOS non directed links are referred to as diffuse links. VLCs can use several link configurations, like directed LOS, non directed LOS and diffuse links, see Figure 1[15].
## 4 Proposed System Design
Local oscillator (LO) and polarization beam splitter (PBS) are deployed for coherent detection that improves the signal to noise ratio. PBS splits the received signal into two orthogonally polarized beams for coherent detection. Both I and Q signals have been passed through a 90\({}^{\circ}\) optical hybrid (OH) followed by an array of photo diodes to detect the information signal. The DSP module has been employed to overcome the channel distortion and compensation of carrier phase noise. Parallel to the serial converter (PSC) gives the resultant demodulated signal. After the detection and decoding of the signal, the BER test set has been employed for direct-error counting.
# A Novel Ensemble Framework Based on K-Means and Resampling for Imbalanced Data
## 3 The Proposed Approach EKR
### The Ensemble Framework based on K-Means and Resampling Technology  
Eventually, after resampling the subsets and combining the sub-cluster and the minority class into k balanced subsets, base classifiers \(\{BC_{1},BC_{2},\ldots,BC_{k}\}\) are trained on each balanced subset, and then integrated into a strong ensemble classifier by voting mechanism. Figure 3 gives the procedure of EKR. Data preprocessing contains K-means and resampling strategy, and ensemble framework refers to the process of training base classifiers on balanced subsets separately and combining them into a strong ensemble classifier.