esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Design of LQ Optimal Controllers
### Design of LQR  
The LQ objective function (LQOF), \(L\), is defined as (15). In the LQOF, control objectives can be tuned by setting the weight, \(\rho\;_{i}\), higher or lower while maintaining the others fixed. In (15), the weight \(\rho\;_{i}\) in \(L\) is tuned by [PERSON]'s rule, (16), where \(\xi_{i}\) is the maximum allowable value (MAV) on \(i\)-th term in \(L\)[17; 18; 19; 20; 46]. For RC, \(\xi_{1}\) on \(a_{zc}\) should be set as small as possible. For MS reduction, \(\xi_{1}\) on \(a_{zc}\) and \(\xi_{3}\) on \(\omega_{yc}\) should be set as small as possible. For road holding, \(\xi_{5}\) on \((d_{p},d_{r})\) and \(\xi_{6}\) on \((w_{f},w_{r})\) should be set as small as possible. With the matrices \(\mathbf{A}\), \(\mathbf{B}_{2}\), \(\mathbf{\Pi}\), \(\mathbf{\Xi}\) and \(\mathbf{\Theta}\) given in (14) and (15), the control input of LQR is computed as the FSF form of (17).  
\[L =\int_{0}^{\infty}\left\{\rho_{1}\tilde{z}_{c}^{2}+\rho_{2}\tilde{ \theta}_{c}^{2}+\rho_{3}\theta_{c}^{2}+\rho_{4}\theta_{c}^{2}+\rho_{5}\sum\limits _{i=f,r}d_{i}^{2}+\rho_{6}\sum\limits_{i=f,r}w_{i}^{2}+\rho_{7}\sum\limits_{i=f,r}u_{i}^{2}\right\}dt \tag{15}\] \[=\int_{0}^{\infty}\left\{\left[\begin{array}{cc}\mathbf{x}\\ \mathbf{u}\end{array}\right]^{T}\left[\begin{array}{cc}\mathbf{\Pi}&\mathbf{ \Xi}\\ \mathbf{\Xi}^{T}&\mathbf{\Theta}\end{array}\right]\left[\begin{array}{c}\mathbf{ x}\\ \mathbf{u}\end{array}\right]\right\}dt,\]  
\[\rho_{i}=\frac{1}{\xi_{i}^{2}},\;i=1,2,\cdots,7, \tag{16}\]  
\[\mathbf{u}=-\mathbf{K}_{LQR}\mathbf{x}=-\mathbf{\Theta}^{-1}\mathbf{B}_{2}^{T }\mathbf{Px}. \tag{17}\]
###### Abstract  
Mangrove is classified as an important ecosystem along the shorelines of tropical and subtropical landmasses, which are being degraded at an alarming rate despite numerous international treaties having been agreed. Iskandar Malaysia (IM) is a fast-growing economic region in southern Peninsula Malaysia, where three Ramsar Sites are located. Since the beginning of the 21 st century (2000-2019), a total loss of 2907.29 ha of mangrove area has been estimated based on medium-high resolution remote sensing data. This corresponds to an annual loss rate of 1.12%, which is higher than the world mangrove depletion rate. The causes of mangrove loss were identified as land conversion to urban, plantations, and aquaculture activities, where large mangrove areas were shattered into many smaller patches. Fragmentation analysis over the mangrove area shows a reduction in the mean patch size (from 105 ha to 27 ha) and an increase in the number of mangrove patches (130 to 402), edge, and shape complexity, where smaller and isolated mangrove patches were found to be related to the rapid development of IM region. The Moderate Resolution Imaging Spectro-radiometer (MODIS) Leaf Area Index (LAI) and Gross Primary Productivity (GPP) products were used to inspect the impact of fragmentation on the mangrove ecosystem process. The mean LAI and GPP of mangrove areas that had not undergone any land cover changes over the years showed an increase from 3.03 to 3.55 (LAI) and 5.81 g C m\({}^{-2}\) to 6.73 g C m\({}^{-2}\) (GPP), highlighting the ability of the mangrove forest to assimilate CO\({}_{2}\) when it is not disturbed. Similarly, GPP also increased over the gained areas (from 1.88 g C m\({}^{-2}\) to 2.78 g C m\({}^{-2}\)). Meanwhile, areas that lost mangroves, but replaced them with oil palm, had decreased mean LAI from 2.99 to 2.62. In fragmented mangrove patches an increase in GPP was recorded, and this could be due to the smaller patches (<9 ha) and their edge effects where abundance of solar radiation along the edges of the patches may increase productivity. The impact on GPP due to fragmentation is found to rely on the type of land transformation and patch characteristics (size, edge, and shape complexity). The preservation of mangrove forests in a rapidly developing region such as IM is vital to ensure ecosystem, ecology, environment, and biodiversity conservation, in addition to providing economical revenue and supporting human activities.  
m  
anguage; coastal region; remote sensing; fragmentation; productivity; land cover change 40/. +
Footnote †: journal: Remote Sensing
# Evaluating the Effectiveness of Large Language Models in Converting Clinical Data to FHIR Format
## 2 Preliminaries
### Impact of Prompts and Temperature  
LLMs offer promising avenues for automating many diverse complex tasks, including converting unstructured clinical reports into standardized FHIR bundles. However, the effectiveness of these models is significantly influenced by two key factors: the design of input prompts and the temperature setting.  
Prompts: The way information is presented to an LLM through prompts plays a crucial role in shaping its output. Different prompting strategies can guide the model's reasoning and influence its ability to accurately extract information and map it to FHIR resources. Broadly, these strategies can be categorized by the level of guidance and examples provided. A basic prompting approach involves providing a direct instruction to the LLMs, such as \"Convert this clinical report to an FHIR bundle\". While simple to implement, basic prompting often relies heavily on the model's pre-existing knowledge and may not provide sufficient context for complex conversion tasks like FHIR.  
More sophisticated or advanced prompting techniques aim to enhance the model's performance by providing additional context, structure, or examples. These techniques can include providing explicit instructions on the desired output format, which helps the model to understand the specific requirements of FHIR bundles. They can also involve incorporating examples of correctly formatted FHIR resources, which allows the model to learn from examples and improve its ability to generate valid bundles. Finally, these advanced techniques can guide the model's reasoning process through step-by-step instructions, which helps the model to break down the complex conversion task into smaller, more manageable steps.  
Temperature Control: Temperature is a hyperparameter that controls the randomness or \"creativity\" of the LLMs' output. A lower temperature setting results in more deterministic and focused responses, prioritizing accuracy and consistency. This is particularly important for tasks like FHIR conversion, where adherence to strict standards is crucial. Conversely, a higher temperature setting introduces more randomness, leading to more diverse and potentially creative outputs, but at the cost of consistency and potentially impacting the validity of the generated FHIR bundles. Therefore, finding an appropriate temperature is essential for balancing the need for accuracy with the potential benefits of exploring diverse conversion strategies.  
The choice of prompting strategy and temperature value has a direct impact on the quality, validity, and completeness of the generated FHIR bundles. By analyzing the interaction between prompts and temperature settings, this study sheds light on the optimal configurations for leveraging LLMs in converting unstructured clinical data to structured FHIR formats, particularly for Spanish clinical cases from different specialties such as skin care and mammography.
## 2 Background and Related Work
### Overview of Related Studies
#### 2.2.1 Topology Management  
The literature offers a wide variety of solutions for modifying the IAB network architecture and protocol stack: mesh-based topologies [20], user-provided networks [21], the use of mmWave unlicensed spectrum [25], moving relays [22; 23; 29; 30], using caching storages [24], novel schemes of network coding [26], a backhaul topology to sustain bursty traffic [27], UAV-based IAB-nodes [28; 31], reconfigurable intelligent surfaces (RISs) [31]. In the context of multi-hop traffic forwarding and QoS management [38], researchers address such issues as autonomous IAB-node activation and dynamic sleeping [32; 33], alternative access control policies [34; 35; 37], and novel beam management strategies [36]. It should also be noted that case studies of IAB cell architectures with mobile IAB-nodes are already under development in Release 18 of the 3 GPP specifications.  
Only a few of the studies mentioned above address the packet level performance of IAB systems. Specifically, in [43], the authors investigated how the density of IAB-nodes in a cell affects the mean number of hops that a packet has to make, provided that a minimum access throughput is provisioned by the network. The IAB cell was compared with a conventional macro-cell deployment having multiple BSs. The authors also assessed the effect of the IAB-donor-to-nodes ratio on the network throughput. It was shown that a dense IAB network outperforms a macro-cell deployment in terms of throughput and also that the number of relaying points is reduced when the network is further densified by IAB-donors.
## 3 Results
### Train-Test Split Models Results  
#### 3.1.1 Emotional Stimuli Dataset  
For the Emotional Stimulus Dataset, a well-balanced dataset comprising 106 samples, each characterized by 20 distinct characteristics, was used. The dataset was partitioned into a training group of 84 samples and a validation group of 22 samples. The Random Forest, Logistic Regression, and AdaBoost models exhibited the most favorable metric values. It is important to note that the metric values range from 0 to 1, with 1 signifying the optimal achievable result. Within the training group, all models demonstrated accuracy and F1 scores that exceeded 80%, some instances reaching as high as 99%. However, in the validation group, as shown in Table 2, the values achieved were significantly lower, with precision and F1 scores consistently below 70%, which indicates overfitting due to poor generalization. Despite this, Random Forest emerged as the best performing algorithm in the validation group, attaining an accuracy rate of 0.64 (64%) and an F1 score of 0.63 (63%).
# Error Source Analysis and Correction of GF-3 Polarimetric Data
## 3 Influence Analysis of Error Source
### Ionosphere Effect
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**TEC** & **Common Value (5–20)** & \
\begin{tabular}{c} **Maximum at Weak Solar** \\ **Activity (about 50)** \\ \end{tabular} &
\begin{tabular}{c} **Maximum at Intense Solar** \\ **Activity (about 100)** \\ \end{tabular} \\ \hline \(B\) & Mean & Maximum & Maximum \\ \(\Omega\) (deg) & \textless{}0.18 & \textless{}0.6 & \textless{}1.21 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Prediction Faraday rotation (FR) of GF-3 satellite. Figure 7: Schematic of electromagnetic wave through the ionosphere.
# Minimum-Cost Offloading for Collaborative Task Execution of MEC-Assisted Platooning
## 6 Numerical Analysis
### Comparison of Execution Patterns  
Figure 9 shows the cost comparison of three execution modes (platoon execution, MEC execution and proposed collaborative execution) and the Constrained Bellman-Ford (CBF) algorithm [37],  
Figure 8: Task offloading decisions. (**a**) \(\alpha_{\text{MEC}}=0.4\); (**b**) \(\alpha_{\text{MEC}}=0.3\); (**c**) \(\alpha_{\text{MEC}}=0.2\); (**d**) \(\alpha_{\text{MEC}}=0.1\).  
Figure 7: Task offloading decisions. (**a**) low computing cycles with high size of data; (**b**) high computing cycles with low size of data; (**c**) low computing cycles with low size of data; (**d**) high computing cycles with high and low size of data.  
\(\alpha_{\mathrm{MEC}}=0.4\). The CBF algorithm could also be adopted to solve this problem, but its computational complexity is \(O\big{(}n^{3}\big{)}\), which is larger than \(O\Big{(}n^{2}\log^{2}n\Big{)}\) in this paper. Additionally, the CBF algorithm gets the optimal solution, while the LARAC algorithm gets the approximate solution. The data sizes and the demanded computation cycles are as follows [19]: \(\{d_{k}\}=\{0\;100\;40\;1\;2\;1\;2\;1\;2\;1\;1\;100\}\) kb, Mcycle. First, compared with MEC server execution, the proposed collaborative execution strategy can greatly reduce the cost. Second, collaborative task execution is more flexible than MEC server execution. Due to the low transmission rate between the leader and the BS, the high transmission delay will be caused by the MEC server execution when the data size is large. Therefore, the tasks with a large amount of transmitted data cannot be completed within the deadline in MEC server execution mode. Third, only collaborative task execution can complete the application less than the deadline 0.21 s, and it costs less than that in the MEC server execution mode. Fourth, with increase of the deadline, the cost of platoon task execution and collaborative task execution are approximately same, because the task execution does not require the participation of MEC server with the release of the deadline. The computational resources provided in the platoon are sufficient to enable the application to be completed within the deadline. The results of the local execution were not drawn, because 3.3 s is the minimum deadline, far from meeting the requirements of the application time constrained. Fifth, we can see that the cost of the collaborative task execution is very close to that of the CBF algorithm, which verifies the proposed algorithm can be well applied in this scenario.
# Channel-Hopping Using Reinforcement Learning for Rendezvous in Asymmetric Cognitive Radio Networks
## 3 Methodology
### Reinforcement Learning: Actor-Critic Algorithm  
In this paper, the agents of SUs are trained by using the actor-critic policy gradient algorithm [15; 16]. First, the policy function, \(\pi_{\theta}\), which generates the action is given by  
\[\theta=\pi_{\theta}(s,a) \tag{1}\]  
where \(s\) and \(a\) denote the state and action for the reinforcement learning. The critic function, \(\hat{q}_{w}\), for the action value is given by  
\[w=\hat{q}_{w}(s,a) \tag{2}\]  
The actor updates the policy parameter using the action value by Equation (3), where \(\Delta\theta\) is the change in policy parameters (weights) and \(\hat{q}_{w}(s,a)\) is the action value estimate.  
\[\Delta\theta=\alpha\
abla_{\theta}(\log\pi_{\theta}(s,a))\hat{q}_{w}(s,a) \tag{3}\]  
where \(\alpha\) represents the learning rate, which is generally chosen as a value within the range of \((0,1)\).  
The following equation describes the update for the critic parameter.  
\[\Delta w=\beta(R(s,a)+\gamma\hat{q}_{w}(S_{t+1},A_{t+1})-\hat{q}_{w}(S_{t},A_{ t}))\Delta_{w}\hat{q}_{w}(S_{t},A_{t}) \tag{4}\]  
In Equation (4), the actor takes an action \(A_{t+1}\) for \(S_{t+1}\) at time \(s+1\), and then the critic updates its value parameters, where \(\beta\), \(R(s,a)\), and \(\gamma\) denote the learning rate, the reward function, and the discount factor. \((R(s,a)+\gamma\hat{q}_{w}(S_{t+1},A_{t+1})-\hat{q}_{w}(S_{t},A_{t}))\) is the temporal difference error and \(\Delta_{w}\hat{q}_{w}(S_{t},A_{t})\) is the gradient of value function.
## 4 Discussion
The robotic rehabilitation activity focused mainly on activities that covered the static and dynamic macroareas, so as to set up a rehabilitation program with increasing difficulty and intensity, with more complex activities to work on dynamic balance and trunk control in scenarios with increased motor demand, according to the patients' residual abilities and the growth of motor skills in the first weeks of treatment. Static and dynamic exercises improved postural skills, particularly sitting balance and trunk control, and transferred these skills to managing trunk stability limits and trunk management when the base of support is reduced, making it more difficult. The use of robotic rehabilitation specifically for trunk control represents an additional tool to prevent or counteract compensatory strategies, supported by postural or movement control, that may be established in patients with trunk control deficits [51; 52]. These compensatory strategies, if not modified, may become reinforced over time, leading to alterations in posture, movement, and difficulty in recovering ambulation and autonomy in performing daily activities. The use of robotic therapy, by providing continuous audio/video feedback and allowing the administration of specific, repetitive, and engaging activities in terms of attention and intensity, has resulted in improved trunk control and balance while seated. The lack of significant improvement in reactive balance could be related to the type of rehabilitation activity performed, both in the experimental group and in the control group; in fact, both groups of patients underwent rehabilitation treatment aimed primarily at improving static and dynamic balance. These results suggest that, complementing what has already been proposed, rehabilitative treatment should include activities to improve reactive balance in order to teach patients how to properly handle perturbations. In light of the results, it can be stated that robotic rehabilitation of trunk and sitting balance with humova in patients with sABI results in improved sitting balance and trunk control under different conditions. This study suggests that humova(r)-based robotic trunk control training may improve the postural skills of patients with sABI more than conventional therapy. The group using the robotic device improved sitting balance, trunk control in static and dynamic situations, functional autonomy, and motor fatigue. The focus on trunk recovery is important, while another topic to consider is how it could affect emotional and cognitive functions more broadly, as these impacts may make rehabilitation considerably more difficult. Developing comprehensive treatment plans requires an understanding of these intricate problems. These results support the effectiveness of robotics applied to neuromotor rehabilitation, indicating that a multidisciplinary and technologically advanced approach could optimize rehabilitation outcomes for patients with sABI.
## 2 Silicon Photonics Micro-Transceiver with Multimode Interface
### Passive Assembly of QD-LD into Silicon Photonics Chip  
As shown in Figure 9a, the QD-LD array (Figure 9b) is passively mounted onto the silicon photonics substrate with silicon pedestals providing vertical alignment, and a fiducial is formed on the substrate to enable visual placement with respect to corresponding alignment marks on the QD-LD chip (Figure 9c) enabling horizontal alignment. Once the QD-LD is aligned, AuSn solder is applied to fix the array in place.  
Figure 8: IOCore laser redundancy circuit: (**a**) conventional silicon photonic circuit scheme with single QD-LD array with four optical outputs; (**b**) second-generation IOCore micro-transceiver with two QD-LD arrays, each with two CW optical outputs [3].  
Figure 9: Passive assembly of QD-LD onto silicon photonics substrate. (**a**) Side cross-sectional schematic showing passive assembly features of QD-LD array onto silicon photonics substrate; (**b**) photo of QD-LD array; (**c**) photo of alignment marks used to visually align QD-LD array.  
This assembly process can be automated with a pick-and-place machine, allowing high-volume, high-yield production.  
Figure 10a shows the statistical distribution of mounting accuracy before and after solder attachment, with 99% (\(\sigma\) = 2.3) of mountings falling within an accuracy of \(\pm\)3 \(\upmu\)m.  
The QD-LD aperture is aligned to a tapered spot size converter (SSC) in the silicon photonic substrate, which allows laser light to couple to the silicon waveguides. Figure 10b shows the coupling loss variation with horizontal and vertical misalignment. The minimum coupling loss between QD-LD laser aperture and the silicon waveguide was measured to be 2 dB. The vertical misalignment tolerance is tighter than the horizontal misalignment tolerance, with a vertical misalignment of 0.5 \(\upmu\)m resulting in -4 dB coupling loss, while a horizontal misalignment of 0.5 \(\upmu\)m resulted in 3 dB coupling loss.  
As described in Section 2.8, changes in temperature give rise to a change in the emission wavelength of the QD-LD, which in turn will also affect the coupling loss, depending on the wavelength dependence of the spot size converter.
## 4 Analysis of Progressive Failure Process of Surrounding Rock and Tunnel Structure  
Due to challenges in measuring the deep displacement and failure zones of surrounding rock over large on-site ranges, numerical calculation methods are employed to study how surrounding rock deformation and failure propagate under various in situ stress conditions. This approach establishes the relationship between the surface deformation of tunnel surrounding rock and the extent of deep failure zones. Surface displacement serves to identify the failure zone's location, offering insights for assessing significant deformation levels and devising appropriate countermeasures.  
Given the engineering context outlined in Section 2, where significant groundwater presence is noted at the tunnel site, this study employs a self-developed finite element method (FEM) integrated with the volume of fluid (VOF) technique for numerical simulations of steeply inclined surrounding rock formations. The VOF method is particularly effective in simulating complex fluid dynamics in regions with extensive groundwater development. It excels in modeling free surface behavior and the interaction between groundwater and excavation activities, making it an optimal choice for addressing fluid boundary conditions such as inflow, outflow, and surface tension effects. The VOF method's capability of accurately tracking fluid free surfaces is crucial for analyzing tunnel stability and construction dynamics. It adeptly manages variations in fluid volumes and interfaces, providing precise descriptions of groundwater flow and its effects on tunnel stability. Additionally, it simulates transient phenomena, such as abrupt changes in groundwater levels, enhancing its utility in assessing construction dynamics.  
Our proprietary software enhances computational efficiency by focusing on fluid volume fractions rather than individual particle tracking, making it particularly suited for simulating large-scale deformations and construction processes involving significant groundwater interactions. The finite element method divides the domain into discrete elements and uses interpolation functions to approximate partial differential equations (PDEs). In fluid-structure interactions (FSI), FEM typically models solid structures, while VOF handles fluid phases. Both methods use iterative solvers to address large systems of equations arising from discretization. Consequently, we aim to seamlessly integrate these methods and develop specialized non-commercial software to address practical engineering challenges presented in this study.  
Figure 14: The dispersion curve of the stable area of the hole (spacing 1.0 m).