esa-sceva/satcom-chunk-collection huggingface dataset:
# A Survey on 5G Coverage Improvement Techniques: Issues and Future Challenges
## 1 Introduction
### Identification of Coverage Issues  
In cellular network, coverage is one of the most important parameters. It is affected by many parameters such as high network density, location of UE and BS, and the environmental conditions. Some of the coverage issues are coverage holes, overshoot coverage, weak coverage, channel coverage mismatch, and the cell edge problems [31]. Coverage hole is a location within the cell boundary where the UE cannot receive signals from the primary cell when it is actually in the primary cell or neighbouring cell if it is at the cell edge. This is mainly due to obstacles such as buildings, trees, mountains, etc., which will reduce the signal strength because of multipath fading effects and because the signal strength received by the UE is less than the threshold value. The coverage holes are mainly due to non-line of sight, poor radio frequency (RF) network planning, and multi-path fading.  
Overshoot coverage is exactly opposite the coverage area. Overshoot coverage occurs when the signal strength is very high and it crosses the primary cell boundary. The issues with overshoot coverage are signal interference and poor quality of service. Weak coverage is the result of the signal strength being below the necessary threshold inside the cell boundary. These two issues can be mitigated by proper adjustment of RF signal power within the cell area. Channel coverage mismatch occurs due to the UL and DL frequency variations. In a cellular network the coverage issues are mainly at cell edge or cell boundary because the signal power decreases exponentially with respect to the distance [32]. To reduce the cell edge issues, sectoring and micro cell concepts were proposed [33; 34].
# A High-Efficiency Method of Mobile Positioning Based on Commercial Vehicle Operation Data
## 4 Analytical Models with Different Outlier Filtering Criteria
### Analytical Models
#### 4.1.2 Availability  
The locations of the same cell-RSSI pair are assumed to be normally distributed, and the availability of the \(i\)-th cell-RSSI pair is defined as \(p_{x,i}\) and \(p_{y,i}\) (see Equations (9) and (10)). The function \(g(z)\) in Equations (9) and (10) is a Gaussian error function, which can be expressed as the Taylor series [37]\(\frac{2}{\sqrt{\pi}}\sum_{n=0}^{\infty}\frac{z}{2n+1}\prod_{k=1}^{n}\frac{=z^{2}}{k}\) s [33]:  
\[\begin{array}{l}p_{x,i}=\int_{\mu_{x,i}-t}^{\mu_{x,i}+t}f\left(x_{i},\mu_{x,i },\sigma_{x,i}\right)dx_{i},\text{ where }f\left(x_{i},\mu_{x,i},\sigma_{x,i}\right)\\ \\ =\frac{1}{\sigma_{x,i}\sqrt{2\pi}}e^{-\frac{\left(x_{i}-\mu_{x,i}\right)^{2}}{2 \sigma_{x,i}^{2}}}\\ \\ =g\left(\frac{t}{\sqrt{2}\sigma_{x,i}\right)}\text{, where }g\left(z\right)= \frac{2}{\sqrt{\pi}}\sum_{n=0}^{\infty}\frac{z}{2n+1}\prod_{k=1}^{n}\frac{ -z^{2}}{k}\\ \\ =\frac{2}{\sqrt{\pi}}\sum_{n=0}^{\infty}\frac{\frac{t}{\sqrt{2}\sigma_{x,i}} }{2n+1}\prod_{k=1}^{n}\frac{-\frac{t}{\sqrt{2}\sigma_{x,i}}^{2}}{k}\\ \\ \end{array} \tag{9}\]\[p_{y,i} =\int_{\mu_{y,i}-t}^{\mu_{y,i}+t}f\left(y_{i},\mu_{y,i},\sigma_{y,i} \right)dy_{i},\text{ where }f\left(y_{i},\mu_{y,i},\sigma_{y,i}\right) \tag{10}\] \[=\frac{1}{\sigma_{y,i}\sqrt{2\pi}}e^{-\frac{\left(y_{i}-\mu_{y,i} \right)^{2}}{2\
u_{y}{}^{2}}}\] \[=g\left(\frac{t}{\sqrt{2}\sigma_{y,i}}\right),\text{ where }g\left(z\right)=\frac{2}{\sqrt{\pi}}\sum\limits_{n=0}^{\infty}\frac{z}{2n+1}\prod \limits_{k=1}^{n}\frac{-z^{2}}{k}\] \[=\frac{2}{\sqrt{\pi}}\sum\limits_{n=0}^{\infty}\frac{\frac{t}{ \sqrt{2}\sigma_{y,i}}}{2n+1}\prod\limits_{k=1}^{n}\frac{-\frac{t}{\sqrt{2}\sigma _{y,i}}^{2}}{k}\]
# Human-Centered AI for Migrant Integration Through LLM and RAG Optimization
## 6 Results and Discussion
### Discussion  
The results obtained from integrating an RAG system into an LLM reveal the critical influence of parameters on the model's performance and the quality of the generated text. In particular, the chunk size and the value of K exhibited a complex interaction, where configurations with smaller chunk sizes (e.g., 50) and lower K values tended to compromise textual cohesion, whereas intermediate chunk sizes (100-300) provided a more balanced trade-off between context integration and computational efficiency.  
As the chunk size and K increased, a decline in the quality of metrics such as the BLEU and ROUGE scores and semantic similarity was observed, suggesting that excessive contextualization can overwhelm the model. Furthermore, using lower temperature values (e.g., 0.1) stabilized text generation, optimizing the precision metrics, while higher temperatures (e.g., 0.9) introduced greater randomness, which degraded textual cohesion.  
In terms of bias and offensive content generation, an increase in the chunk size and K led to higher stereotype scores and a reduction in the model's ability to produce neutral and non-offensive responses. This effect was more pronounced at higher temperatures, emphasizing the importance of precise temperature control in bias-sensitive applications.  
The application of MCDM methods, including the RIM, TOPSIS, and VIKOR, offers a comprehensive framework for identifying optimal configurations. These methods highlight discrepancies in the results due to the differing underlying philosophies of each approach. They facilitate the analysis and selection of the most suitable parameters for performance optimization, in alignment with the hypothesis that integrating MCDM methods in the optimization of hyperparameters for RAG and LLM systems enhances their effectiveness and ethical alignment. This approach ensures the generation of more inclusive, fair, and contextually appropriate responses tailored to the needs of vulnerable populations, such as migrants and refugees. Consequently, these methods provide a valuable framework for evaluating trade-offs between performance and efficiency, supporting the development of RAG systems which adhere to human-centered artificial intelligence principles.  
Figure 8: Comparison of CO\({}_{2}\)e emissions between models which do not utilize integration processes and those which incorporate such processes. The values represent the median CO\({}_{2}\)e emissions calculated for each combination of the K and temperature parameters.  
The integration of processes for bias and hate speech mitigation in language models presents clear advantages in terms of ethics and sustainability. However, these improvements are accompanied by a potential decrease in accuracy and an increase in computational complexity and cost. The choice between process integration and non-integration should be based on the specific priorities of the project, balancing accuracy with ethical and environmental responsibility.
## 2 LDS-UFMC System Model
### LDS Spreader  
Consider an LDS-UFMC system with \(K\) users and the users are indexed as follows; \(k=1,...,K\) and all users are assumed to transmit the same number of data streams \(M\) and they are indexed as follows; \(m=1,...,M\). Assume the number of subcarriers is \(N\), and they are indexed as follows; \(n=1,...,N\). We define the spreading matrix of the \(k\)th user as:  
\[\mathbf{S_{k}}=[s_{\mathbf{k,1}},...,s_{\mathbf{k,M}}]\in\mathbb{C}^{N\times M} \tag{1}\]  
\(\mathbf{s_{k,m}}=[s_{k,m}^{1},...,s_{k,m}^{N}]^{T}\) is the sparse vector of length \(N\) used to spread the \(m\)th symbol of the \(k\)th user. Thus, the matrix of spreading of all users can be represented as :  
\[\mathbf{S}=[\mathbf{S_{1}},...,\mathbf{S_{K}}]\in\mathbb{C}^{N\times MK} \tag{2}\]  
Thanks to the sparse nature of this matrix, only a small number of users can share the same subcarrier, we define it as \(d_{c}\) the interference degree. Hence, among \(N\) subcarriers only \(d_{v}\) will be used to serve one user, \(d_{v}\) is called the effective spreading gain. Unlike the conventional CDMA system, we require \(d_{v}\ll N\) and \(d_{c}\ll K\). Let \(\mathbf{a_{k}}\) be user's \(k\) symbols:  
\[\mathbf{a_{k}}=[a_{k,1},...,a_{k,M}]^{T} \tag{3}\]  
Figure 1: Low Density Signature-Universal Filtered Multi-Carrier (LDS-UFMC) block diagram.  
After the spreading process, the signal of the \(k\)th user \(\mathbf{x_{k}}=[x_{k}^{1},...,x_{k}^{N}]^{T}\) is a vector of length \(N\) and can be written as follows:  
\[\mathbf{x_{k}}=\mathbf{S_{k}}\times\mathbf{a_{k}} \tag{4}\]  
and \(x_{k}^{n}\) will be the data transmitted over the \(n\)th subcarrier by the \(k\)th user:  
\[x_{k}^{n}=\sum_{m=1}^{M}a_{k,m}s_{k,m}^{n} \tag{5}\]  
Hence, the signal transmitted over the \(n\)th subcarrier is given by (6):  
\[x^{n}=\sum_{k\in\xi_{n}}x_{k}^{n} \tag{6}\]  
\(\xi_{n}\) is considered as the group of users interfering in the \(n\)th subcarrier.  
Figure 2 represents the spreading process of the LDS. In this example, we consider that each user transmits one symbol (i.e., \(M=1\)). The system transmits over 4 subcarriers and serves 8 users which means that the overloading is at 200%. Each subcarrier is allocated to 4 users (\(d_{c}=4\)), and each user spreads its data over 2 subcarriers (\(d_{v}=2\)). The symbols are the variable nodes and the subcarriers are the functions nodes respectively. The edges that connect the nodes define if a symbol will be spreaded over the adjacent subcarrier. For instance, the first user is connected to subcarriers 1 and 2, consequently, the first symbol will be spreaded over these adjacent subcarriers only. This representation of the spreading process is called the Tanner Graph [16].
## 4 Discussion
SwCA have been described to increase muscle coactivation as a global compensatory mechanism to deal with the enlarged lateral body sway caused by a lack of inter-joint coordination and hypotonia, which impairs their ability to recover energy while walking [11]. In this way, it is possible to argue that using the less flexible exoskeleton, by allowing a more efficient walking behavior and reducing CoM displacement, reduces the need for swCA to increase the muscle coactivation and allows better coordination between muscle activations, as expressed by _CMC_ values. Another datum that well explains the effectiveness of the device is associated with the reduction of the variability monitored in the conditions of use of the exoskeleton compared to that of non-use. The increased variability between gait cycles is a typical characteristic of atax patients [53] and having the possibility of reducing it means offering them the possibility of moving in three-dimensional space more efficiently and safely. As this was a pilot study, no sample size analysis was performed prior to recruitment. This represents a limitation of this study, together with the small sample size. In this way, the results of this study should be interpreted cautiously. However, the findings of this study may allow us to conduct further studies with larger samples, and also to analyze any differences in gender and decades of age. It will also be necessary to analyze the behavior of the forces exchanged with the environment. This also allows us to better understand the strategies adopted by the central nervous system in managing the interaction with the exoskeleton. Another limitation of this study is that we only examined the gait of swCA who could walk independently, excluding subjects with a more severe disability, to which our findings cannot be directly applied. Furthermore, we were unable to conduct subgroup analysis to highlight differences between types of degenerative CA, and we only evaluated subjects with inherited degenerative CA, ruling out subjects with other cerebellar disorders. Furthermore, we only investigated linear walking in the steady state. Although walking on a 10 m walkway may be sufficient to assess the walking capacity of swCA [54; 55], they also experience dynamic unbalance during transitions or externally perturbed conditions. Therefore, additional studies should be conducted to explore the effects of the device during more demanding locomotor tasks such as curvilinear and tandem walking, gait initiation and termination, etc. [56]  
Further studies will also be necessary for medium- and long-term evaluations of wearing these devices for long periods, to also evaluate thermal comfort through the use of a thermal manikin, and to analyze confounding factors which would have been possible if we had also investigated the walking of the control subjects wearing the exoskeleton.
# IFF-Net: Irregular Feature Fusion Network for Multimodal Remote Sensing Image Classification
## 3 Results
### Results and Analysis on Berlin Data  
(1) Quantitative comparison: From the experimental results listed in Table 8, it is evident that multimodal remote sensing feature fusion can improve classification accuracy. The classification accuracy of the classifier using HSIs or SAR images alone is less than that of the classifier using HSI and SAR fusion. We speculate that the simple data superposition fusion method cannot eliminate the noise and makes the classification effect of SAR images worse. Fusion of spatial and spectral information facilitates image interpretation. As can be seen from Table 8, the classification accuracy of deep-learning-based classifiers is better, and the CapsNet classifier outperforms the traditional SVM classifier, S2 FL classifier, and LeMA classifier in terms of classification performance. The OA of Two-branch CNN is improved, which is slightly better than that of the CapsNet classifier, especially in the classification accuracy of C1 and C2. Compared with other classifiers, the OA of the IFF-Net classifier proposed in this paper is increased by +7.77%, +3.97%, +4.81%, +3.57%, and 14.96%, respectively. Among them, the IFF-Net classification method achieves the best classification accuracy on single classes C3, C4, C5, C6, C7, and C8. Overall, the IFF-Net method proposed in this paper has the best classification accuracy compared with other methods. It makes the feature information fully fused by the irregular feature fusion strategy, which improves the classification accuracy. In order to evaluate the stability  
Figure 5: Classification maps of different multimodality methods on the Augsburg dataset.
## 1 Introduction
In this regard, construction of a selective solar absorber can be based on a number of concepts [23]. One of the available concepts is a thickness insensitive coating, in which low-emissivity flakes are embedded in a suitable binder with a pigment with high solar absorption. Such a coating can be applied on various substrates; however, its low emissivity and price are questionable. Another concept utilizes a thickness sensitive coating. This requires a low-emissivity surface (such as aluminum) covered by a layer of material with high absorption of shortwave radiation and, simultaneously, transparency for longwave radiation. The thickness of the coating is usually less than 5 microns. Such a thickness can be easily achieved by \"high-tech\" methods such as vacuum deposition [24; 25] or ion deposition. These methods can produce an absorbent layer with a thickness of about 40 nm, which is sufficient to reduce surface emissivity below \(\varepsilon=0.05\). Such technology is commonly used, e.g., in the packaging industry [26], microelectronics, photovoltaics or optics [27; 28]. However, \"low-tech\" methods also exist, such as painting [29; 30], which can be sufficiently accurate (for the intended purpose) in the controlled environment of an industrial production line. Other options include chemical deposition [31] or treatment of the low-emissivity substrate [23]. However, these have disadvantages, such as the wet process or potentially harmful chemicals. Finally, solar absorbers can be based on a thin polymer foil dyed with suitable dark pigment. This foil can be attached to a low-emissivity surface or directly metalized, e.g., by vacuum deposition. In conventional building applications (solar thermal systems, solar walls), the metal is also present as a structural element in the form of a sheet (metal thickness of at least 0.2 mm). However, previous research identified high environmental impacts of metal in this role [32; 33]. Therefore, the developed absorber presented in this paper utilizes metal only for its impact on emissivity. The structural role is transferred to the underlying material to which it will be attached. Dyed polyolefin laminate backed by a low-emissivity substrate was selected as a relevant option for such a low-cost environmentally-friendly solution. Another important aspect is that although a need may exist for a specific industrial production line (such as an industrial line for blowing-out the foil and in-line vacuum deposition facility), it is possible to purchase machine time, thus allowing the production to be managed without significant capital investment. Low emissivity barriers [34] and reflective foils [35] have been widely investigated in buildings when opaque building components are designed. However, although existing works (such as [36; 37]) suggest that a solar selective absorber is a viable option for the reduction of heating energy consumption, a literature review did not identify an existing solar absorber foil that could be potentially integrated for innovative energy-efficient building-envelope applications.
# Tightly-Coupled GNSS/Vision Using a Sky-Pointing Camera for Vehicle Navigation in Urban Areas
## 3 Vehicle Motion Estimation Using an Upward-Facing Camera  
This section discusses the steps required for vehicle ego-motion estimation.
## 3 Results and Discussion  
### Electroration Efficiency  
Figure 1 presents the cell disintegration index of pumpkin treated with different specific energy inputs (\(W_{p}\) = 0-64 kJ/kg). The application of PEF even at a low energy of 0.1-1 kJ/kg induces the disintegration of pumpkin cells. The application of PEF treatment with a specific energy higher than 4 kJ/kg practically did not increase \(Z_{p}\), as a so-called plateau was reached. These data are in good correspondence with previously reported findings for other different fruits and vegetables that suggest that plant matrices can achieve a so-called saturation level of electroporation [31; 32]. Relying on the values of \(Z_{p}\), the PEF treatments at low and medium intensities (0.1, 0.5, 1, and 2 kJ/kg) were selected for further freeze-drying, later referred as PEF1, PEF2, PEF3, and PEF4, respectively.  
Figure 1: Cell disintegration index of pumpkin, \(Z_{p}\), treated at different PEF specific energy inputs, \(W_{p}\).
## 3 Results and Discussion
### Validation
#### 3.1.3 Discussion  
Despite uncertain factors in the experiment, the experimental results still show similarities with the numerical model. Note that both the results are in the same scale and the vibrations dissipated in a similar pattern. The fundamental natural frequency of the physical model (\(f_{1}^{exp}=3.87\) Hz) is 12.07% higher than that numerically predicted (\(f_{1}^{num}=3.45\) Hz), where the natural frequencies are calculated using the elementary mechanical vibration theory. Besides the uncertainties of material properties, another potential factor contributing to the discrepancy was the steel joints at the corners of the physical structure. The steel connections were realized by L-shaped angle steels and bolts. These components added additional thickness to the structure and hence increased the flexural stiffness of the whole structure, resulting in higher fundamental natural frequency of the entire structure. In addition, the calculated and experimental \(DI\) exhibited similar trend. The \(DI\) was greater when the damage was more severe. In the subfigures of partial enlargement focusing on the peaks, the gaps between expanded and measured strain of DL2 are larger than those of DL1. Since the reduced stiffness of the beam with a larger crack amplifies the strain response, the difference of the strains increases when comparing with the health beam. The different levels of \(DI\) confirmed that the SER approach can effectively identify the local damage in a slat. However, the calculated \(DI\) was more sensitive than the experimental \(DI\).  
The main reason for this observation was the difference between the numerical model and actual structure. An extremely low value of \(D_{0}\) can be obtained using proper boundary conditions to eliminate interference. For the experimental \(D\), because of the sensitivity limitation in measurements, the measured strains were lower than the calculated values. Because the \(DI\) was obtained by calculating \(D\)/\(D_{0}\), the low \(D_{0}\) and large \(D\) in the simulation generated a large calculated \(DI\). Similarly, the experimental \(DI\) was lower than the calculated \(DI\) because of the large \(D_{0}\) and low \(D\) in measurements. Nonetheless, the SER approach can effectively identify damage because the differences between DL2, DL1, and INT in the experimental results were sufficiently large. The experimental \(DI\) of DL2 was 158% of that of DL1, whereas the experimental \(DI\) of DL1 was 359% of that of INT.
# Free Space Optical Communication: An Enabling Backhaul Technology for 6G Non-Terrestrial Networks
## 4 Challenges and the Road Ahead  
FSO has emerged as a key enabler for wireless connectivity in the future NTNs. In the previous sections, we have provided a comprehensive discussion on backhauling system architecture, physical layer techniques, and self-sustainability. There has been a growing literature on airborne FSO systems, exploring advanced concepts for improved performance and extended transmission ranges. In the following, we provide a summary of current challenges and possible solutions.