esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
Although the present study has important strengths (e.g., the inclusion of several lifestyle characteristics and cardiovascular risk indicators), certain limitations need to be considered. First, the sample size was not large, which means false positive and false negative findings could not be excluded. Second, we used the convenience sample of outpatients, and representativeness could therefore not be evaluated. Moreover, we limited adjustment for medication effects to the total dosage of antipsychotics on the day of enrollment and did not analyze other characteristics of pharmacotherapy (e.g., lifetime exposure to antipsychotics, the use of specific antipsychotics or other psychiatric medications). Additionally, recording quality of sleep may increase insights into lifestyle characteristics. Therefore, our results may not be generalizable to the whole population of patients with schizophrenia. Another limitation is that we did not adopt a longitudinal design, and conclusions regarding causality could therefore not be established. Finally, the use of self-reports to record lifestyle characteristics can also be perceived as a limitation due to a risk of recall bias. In summary, the findings for our study indicate that patients with D-SCZ, as opposed to those with ND-SCZ, might show elevated CRP levels, even when lifestyle characteristics are controlled. However, additional studies are still required to identify the mechanisms of subclinical inflammation in patients with D-SCZ. Moreover, the two groups of patients did not differ in terms of lipid profile or glucose homeostasis. From a clinical perspective, these findings indicate that both groups of patients require monitoring of cardiovascular risk. **Author Contributions:** Conceptualization, A.C. and B.M. ; methodology, B.M. ; formal analysis, B.M. ; investigation, A.C., P.P., J.S., A.S., E.T. and L.L. ; data curation, B.M. ; writing--original draft preparation, A.C.; writing--review & editing, B.M., P.P. and J.S. ; visualization, B.M. ; supervision, B.M. ; project administration, A.C. and P.P. ; funding acquisition, B.M. All authors have read and agreed to the published version of the manuscript. **Funding:** This study was funded by the OPUS grant awarded by the National Science Centre (grant number: 2018/31/B/NZ5/00527). **Institutional Review Board Statement:** The study was carried out according to the guidelines of the Declaration of Helsinki, and approved by Ethics Committee at Wroclaw Medical University (Wroclaw, Poland, approval number: 512/2019) and Pomeranian Medical University (Szczecin, Poland, approval number: KB-0012/130/2019). All participants signed written informed consent. **Informed Consent Statement:** Informed consent was obtained from all subjects involved in the study. **Data Availability Statement:** The data obtained in the study are available upon reasonable request sent to the corresponding author. **Conflicts of Interest:** The authors declare no conflict of interest.
# A Survey on Change Detection and Time Series Analysis with Applications
## 2 Decomposition Methods into Frequency Domain
### Recent Methods of Mitigating Spectral Leakages in Spectrum Beyond Aliasing
#### 2.4.2 Multichannel AntiLeakage Least-Squares Spectral Analysis (MALLSSA)  
The MALLSSA uses the idea of MIMAP to estimate a frequency spectrum for a coarsely sampled time series with the aid of its corresponding gradients [27]. Unlike MIMAP, the cost function in MALLSSA is defined in such a way that it considers the covariance matrices associated with the time series and its gradients and also can get updated iteratively to simultaneously estimate the statistically significant spectral peaks. Since MALLSSA is based on ALLSSA, it inherits all the advantages of ALLSSA.  
The MALLSSA, like MIMAP or almost any regularization method in the presence of aliasing, has the potential overlap of two or more spectral replicas at the same fre  
Figure 2: (**a**) Two aliased sinusoids with identical values at sampling frequency 32 Hz, and (**b**) their gradients with many different values at sample positions.  
quency. The perfect overlap of aliased events happens only in the ideal case of perfectly regular sampling.
## 5 Results and Discussion
### Experimental Setup  
The same experimental settings were used to fairly compare the energy efficiency of the evaluated protocols by adopting similar parameters to those from most studies [6; 7; 8; 9; 10; 11; 12; 13; 14; 15; 16; 17]. The experimental parameters are summarized in Table 2.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Number of nodes & 100 \\ Network size & 100 \(\times\) 100 m \\ Location of the BS & (50,150) m \\ Homogeneous initial energy (\(\mathbf{E_{0}}\)) & 1 J \\ Heterogeneous initial energy (\(\mathbf{E_{\text{het}}}\)) & Rand(0,5,1) J \\ Radio electronics energy (\(\mathbf{E_{\text{elec}}}\)) & 50 nJ/bit \\ Free-space channel parameter (\(\mathbf{\epsilon_{\text{fs}}}\)) & 10 pJ/bit/m\({}^{2}\) \\ Multipath channel parameter (\(\mathbf{\epsilon_{mp}}\)) & 0.0013 pJ/bit/m\({}^{4}\) \\ Data aggregation energy (\(\mathbf{E_{\text{DA}}}\)) & 5 nJ/bit \\ CH selection probability (\(\mathbf{P_{\text{CH}}}\)) & 5\% \\ Length of the message sent from node to CH & 2800 bits \\ Length of packets sent from CH to the BS & 6400 bits \\ \hline \hline \end{tabular}
\end{table}
Table 2: Network parameters for the evaluation of CH selection protocols.  
Figure 4: SSMOECHS network topology and CH distribution.  
The other parameters were similar to those in literature, except for the BS location, which varied depending on the purpose of each analysis. For instance, it was set to (50,50) m in [6; 18], (50,150) m in [16; 24], and (50,175) m in [7; 8; 9]. The distance from the node varied depending on the BS location and determined the transmission model (Equation (14)). Calculating \(d_{0}\) using Equation (15) and the values of \(\epsilon_{\text{fs}}\) and \(\epsilon_{\text{mp}}\) in Table 2 retrieved a threshold of 87.706 m. When the BS was located at (50,50) m, the distance to the farthest node was 70.71 m. Therefore, all nodes sent data using the free-space model. When the BS was located at (50,175) m, the distance to every node ranged from 50 to 182 m, and hence most nodes used the multipath model. We located the BS at (50,150) m to have a ratio of 1:2 for using the free-space and multipath models, thus evaluating both models. In the SMOTECP [18] protocol, the location of the BS was set to (50,50) m. Thus, SMOTECP determined all CHs as external CHs when the location of the BS was (50,150) m. Therefore, the criteria for selecting inner CHs and outer CHs described in [18] should be changed. For a fair experimental environment, after selecting the CHs in the manner shown in [18], the inner and outer CHs were determined as shown in Figure 3b. SSMOECHS also requires swarm parameters for sampling-based SMO, and these parameters are listed in Table 3. These parameters were also used for PSO-C and SMOTECP.
## 4 Analysis and Discussion
### Inspection Context and Industrial Sectors  
There are many different industry sectors that apply AVI in maintenance and manufacturing. In maintenance, the clear majority (66.88%) of publications are from the civil engineering sector, while, in manufacturing, the distribution of a similar total share is more even, with 26.03% from (micro-) electronics, 17.81% from wrought material production, 15.07% from agriculture, and 12.33% from automotive. This may be due to the fact that many manufacturing applications were already targeted for automation with classic CV, as the environmental conditions that affect image quality, as well as variance, are easier to control, so the expected process improvements were generally lower. Most maintenance use cases are performed outdoors in varying light, weather, and seasonal conditions. Therefore, DL-based CV was needed to address use cases in the civil engineering or railway sector, such as bridge, road, or building facade inspection, as well as rail surface, fastener, or catenary inspection. Looking at the preferred AVI tasks, we see a clear majority of 77.01% for localization in maintenance, while, in manufacturing, the shares of classification (50.47%)  
\begin{table}
\begin{tabular}{l l l l} \hline
**VI Use Case** & **Model** & **Count** & **References** \\ \hline \multirow{8}{*}{Crack Detection} & AlexNet & 1 & [166] \\  & DeepLab & 2 & [61, 167] \\  & FCN & 1 & [168] \\  & Mask RCNN & 5 & [169, 170, 171, 172, 173] \\  & UNet & 1 & [174] \\  & YOLO & 5 & [175, 176, 177, 178, 179] \\ \hline \multirow{8}{*}{Damage Detection} & CNN & 7 & [180, 181, 182, 183, 184, 185, 186] \\  & DETR & 1 & [187] \\  & EfficientNet & 1 & [188] \\  & FCN & 3 & [189, 190, 191] \\  & FCOS & 1 & [192] \\  & Faster RCNN & 10 & [193, 200, 201, 202] \\  & Mask RCNN & 6 & [53, 203, 204, 205, 206, 207] \\  & MobileNet & 1 & [39] \\  & RCNN & 1 & [45] \\  & SSD & 5 & [208, 209, 210, 211, 212] \\  & Swin & 1 & [164] \\  & UNet & 4 & [58, 213, 214, 215] \\  & VGG & 1 & [216] \\  & YOLO & 16 & [41, 57, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230] \\ \hline \multirow{8}{*}{Completeness Check} & CNN & 1 & [231] \\  & Ensemble & 1 & [232] \\ \cline{1-1}  & Faster RCNN & 2 & [233, 234] \\ \cline{1-1}  & YOLO & 2 & [42, 52] \\ \hline Quality Inspection & YOLO & 3 & [40, 235, 236] \\ \cline{1-1} Other & YOLO & 1 & [165] \\ \hline \end{tabular}
\end{table}
Table 5: Overview of VI use cases and models that solve these use cases via multi-class localization.  
and localization (49.53%) are evenly split. The reason for this is that, in maintenance, it is usually necessary to assess a whole system with many different parts, so the need for localization arises, while, in manufacturing, it is easier to limit the inspection to individual parts, which is also possible with classification.
# Resource Management in Cloud Radio Access Network: Conventional and New Approaches
## 3 Resource Management Techniques in C-RAN
### Computational Resource Management Based on RRH Clustering Techniques
#### 3.2.5 Throughput-Aware RRH Clustering  
[PERSON] et al. investigated a throughput-aware RRH clustering problem with the objective of maximizing the throughput for end-users by maintaining multiple constraints on BBU resources [63]. A two-stage approach was proposed. Here, the throughput value and requirements of each RHH were calculated first considering the SINR values and the distance of RRH and users. Then, a k-dimensional multiple-choice knapsack problem (k-MCKP) was formulated considering the calculated result as inputs. Unlike previous works, this work considers multiple BBUs with several constraints, based on real hardware implementation. The arrival of UEs was modeled by a Poisson point process, in which UEs were spread and connected to RRHs. They proposed a heuristic to solve the k-MCKP, which was simple and efficient, and obtain values close to the optimal solution.
# Automatic Methodology for Forest Fire Mapping with SuperDove Imagery
## 4 Discussion
Since the proposed method is based on vegetation indices, these variations in reflectance could lead to misinterpretations in burned-area calculations, especially for larger fires that span a broader range of soil types. Additionally, the radiometric noise inherent to the SuperDove sensor data could also contribute to these inaccuracies. More research is needed to explore alternative approaches or data-correction techniques that can take these limitations into account and improve the accuracy of burned-area delineation, particularly in regions with diverse soil compositions. The fire-severity maps generated using the clustering algorithm identified burned zones that aligned with the Copernicus Emergency Management Service reports. However, there were significant discrepancies in the areas assigned to each severity level. This difference presumably arises from the different methodologies used. The proposed approach is based on a clustering algorithm that groups pixels into four predefined severity categories. In contrast, the CEMS uses established thresholds applied to the NDVI. While the automatic method offers flexibility, it may not be tailored as effectively to specific fire events compared to the standardized thresholds used by the CEMS. Unfortunately, the absence of field data in the study areas makes it difficult to definitively determine which approach is more accurate. Despite this limitation, the severity maps derived from the proposed methodology capture the general trends observed in the CEMS reports. To fully understand the discrepancies and validate the accuracy of each method, further research with ground validation data is necessary.
## 7 Experiments Results and Analysis
### Experiment II: Parameter Analysis of User Preference Modeling  
As mentioned above, we jointly modeled user social tag preference and user deep feature preference into one model to combine the advantages of social tags with deep features. However, it is difficult for the weight lambda of user preference modeling for social tags to have an optimal value mathematically, therefore an experiment is implemented to decide the value of lambda. Different lambda such as 0.05, 0.1, 0.2, 0.4, 0.6, and 0.8 are used to recommend 10 videos to each of the 10 users with different backgrounds. Then, their interested videos are recorded to analyze which lambda can get the best recommended performance. The final results are shown in Figure 11. From the experimental results we can see that the interested videos of the recommendation results are optimal when the lambda is equal to 0.35 approximately. Therefore, lambda is set to 0.35 in our method.  
Experiment III: Correlation Coefficient Comparison Results between the Predictive User Preference and the Real User Preference  
In the field of user preference modeling, the Pearson correlation coefficient between the predictive user preference and real user preference is often used to verify the superiority of the user preference modeling method. The Pearson correlation coefficient \(r\) is defined as  
\[r=\frac{\sum XY-\frac{\sum X\
abla Y}{N}}{\sqrt{\sum X^{2}-\frac{(\sum X^{2})} {N}}\sqrt{\sum Y^{2}-\frac{(\sum Y^{2})}{N}}} \tag{20}\]  
where \(N\) is the number of mobile video categories, \(X\) is the user real preferences for each category, and \(Y\) is the predictive user preferences for each category.  
Meanwhile, in order to ensure the fairness and feasibility of the experiment, 10 users with different backgrounds were tracked in the experiment for one month. Their preferences were modeled based on their viewing behavior. Then the total number of videos viewed by each user was counted. The users' Pearson correlation coefficients for this category are shown in Table 2 and Figure 12. It can be seen that the correlation coefficient of our method between the predictive user preference and the real user preference is the highest. The average Pearson correlation coefficient of 10 users is 0.836 in our method, which is much higher than the 0.621 of the video topic methods and is lightly higher than 0.803 of the tag ranking method. At the same time, the superiority of our method can be seen more intuitively from Figure 12. This is because our method fully realizes the complementarity of users' different levels of information and can effectively describe user preference.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Methods** & **Differential Evolution Algorithm** & **Our Method** \\ \hline Time & 10 s & 5 s \\ \hline \hline \end{tabular}
\end{table}
Table 1: The running time results of two methods.  
Figure 11: The relationship between the lambda and the interested videos.
# An Enhanced Belief Propagation Flipping Decoder for Polar Codes with Stepping Strategy
## 2 Preliminary
### Polar Code  
After channel combining and channel splitting, \(N\) independent copies of binary-input discrete memoryless channels are converted to \(N\) split channels with different capacities [18]. Some of these have a high channel capacity, which means that the channel is more reliable for transmitting information, and some of them have a low capacity. Polar codes use high-capacity channels to transmit information bits and CRC bits, and the rest of the channels are used to transmit frozen bits. In this paper, the frozen bits are fixed to zero.  
Polar code can be represented as \(P\) (\(N\), \(K\)), where \(N\) is the code length of the polar code and \(K\) represents the length of the information bits. Meanwhile, \((N-K)\) represents the length of frozen bits. The code rate is \(R\)=\(K/N\). The \(K\) information bits comprise \((K-r)\)-bit data and \(r\)-bit CRC. The set of information bits and frozen bits are denoted as \(\mathcal{A}\) and \(\mathcal{A}^{C}\), respectively. The encoding process can be expressed as  
\[\mathbf{x}_{1}^{N}=\mathbf{u}_{1}^{N}\mathbf{G}_{N}, \tag{1}\]  
where \(\mathbf{x}_{1}^{N}=\{x_{1},x_{2},\ldots,x_{N}\}\) represents the codeword and \(\mathbf{u}_{1}^{N}=\{u_{1},u_{2},\ldots,u_{N}\}\) denotes the source vector which is mixed with the information bits \(u_{\mathcal{A}}\) and the frozen bits \(u_{\mathcal{A}^{C}}\). The generator matrix is represented as \(\mathbf{G}_{N}\)=\(\mathbf{B}_{N}\mathbf{F}^{\otimes n}\), where \(\mathbf{B}_{N}\) is the bit-reversal permutation matrix [20], and \(\mathbf{F}^{\otimes n}\) denotes the \(n\)-th Kronecker power of \(n=log_{2}N\) and  
\[\mathbf{F}=\left[\begin{array}{cc}1&0\\ 1&1\end{array}\right]. \tag{2}\]  
The BP decoding is initiated from the received value \(\mathbf{y}_{1}^{N}\)=\(\{y_{1},y_{2},\ldots,y_{N}\}\). The decoder generates an estimation \(\hat{u}_{i}\) of \(u_{i}\) based on the received \(\mathbf{y}_{1}^{N}\) as  
\[\hat{u}_{i}=\left\{\begin{array}{ll}0,&\text{if }i\in\mathcal{A}^{C},\\ 0,&\text{if }i\in\mathcal{A}\text{ and }\frac{W_{N}^{(i)}(y_{1}^{N},\hat{u}_{1}^{i-1} |u_{i}=0)}{W_{N}^{(i)}(y_{1}^{N},\hat{u}_{1}^{i-1}|u_{i}=1)}\geq 1,\\ 1,&\text{if }i\in\mathcal{A}\text{ and }\frac{W_{N}^{(i)}(y_{1}^{N},\hat{u}_{1}^{i-1} |u_{i}=0)}{W_{N}^{(i)}(y_{1}^{N},\hat{u}_{1}^{i-1}|u_{i}=1)}<1.\end{array}\right. \tag{3}\]  
A binary input memoryless channel \(W_{n}\) generates \(N\) sub-channels by channel splitting, defining \(W_{n}^{(i)}\left(y_{1}^{N},\hat{u}_{1}^{i-1}\middle|u_{i}\right)\), \(i=1,2,\ldots,N\). Let the LLR of \(W_{n}^{(i)}\left(y_{1}^{N},\hat{u}_{1}^{i-1}\middle|u_{i}\right)\) be defined as  
\[\mathcal{L}_{n}^{(i)}\left(y_{1}^{N},\hat{u}_{1}^{i-1}\right)=\ln\left(\frac{ W_{n}^{(i)}(y_{1}^{N},\hat{u}_{1}^{i-1}|u_{i}=0)}{W_{n}^{(i)}(y_{1}^{N},\hat{u}_{1} ^{i-1}|u_{i}=1)}\right). \tag{4}\]
## 1 Introduction  
From the development of traditional analog TV to the recent digital TV, digital video advertising billboards, smart phones and car audio and video equipment, digital video have brought convenience to human life. The human desire for clear images promotes the continuous improvement of the resolution of digital images. The Consumer Technology Association (CTA) announced in December 2019 the industry-recognized standard and official designation for 8K UHD TVs, which will be used from 2020 in models to help retailers and consumers identify compliant technologies Certified 8K ultra HD requirements [1]. With the growth of the resolution of digital images, scholars in the field of digital image processing have not only improved image quality optimization techniques such as color correction and noise reduction but also developed a variety of image processing techniques such as object recognition and image tracking. Object tracking is a fast-growing technology in the field of image processing, computer vision, and robotics. Object tracking has many practical applications in life, such as self-driving cars that automatically brake to avoid collisions [2, 3, 4, 5]. Another example is the use of face recognition when entering and exiting subway gates in order to maintain the safety of passengers [6, 7, 8, 9]. Object tracking includes single object tracking (SOT) and multiple object tracking (MOT). The main difference between the single object tracking problem and the multiple object tracking problem is whether there are one or more moving objects in the image. The focus of SOT is on high tracking accuracy. Therefore, it is necessary to distinguish the background from the foreground and overcome the factors that affect the tracking accuracy including deformation, illumination changes, motion blur, and occlusion. The MOT is committed to distinguishing multiple objects, so it is necessary to distinguish the differences between multiple objects and deal with the association of multiple objects in adjacent frame images [10; 11; 12]. The MOT is built on the basis of good SOT. Even if there is only one tracking object, the importance of SOT cannot be ignored. So far, scholars are still trying to solve the four difficulties encountered in SOT, namely deformation, illumination variation, motion blur, and occlusion.
## 1 Introduction  
### Context  
Nowadays, the urban climate is being extensively studied by the scientific community due to the different effects related to climate change [1]. One of the most notorious is the general rise in temperatures in urban environments caused by massive urbanization, rapid growth in the population and buildings designed with poor environmental adaptation awareness [2, 3]. This leads to heat accumulation problems in homes during periods of high temperatures. This presents a significant challenge, as it can result in adverse conditions for occupants, including discomfort, reduced indoor air quality, and increased building energy consumption. Implementing effective ventilation strategies is essential to dissipate accumulated heat and enhance indoor thermal comfort during the cooler hours, countering the adverse effects of daytime heat accumulation.  
In this context, several cooling techniques have been proposed throughout the years, and some of them have become part of our daily life [4, 5]. On the other hand, passive cooling methods are gaining popularity as they take advantage of the environmental conditions to achieve the desired temperature indoors without polluting or having a major impact on the environment [6; 7]. Among them, the ventilation of a room by means of opening windows and doors is an intuitive solution, and its impact on the thermal comfort of a room has been well documented. This approach is named natural ventilation [8]. Among these natural ventilation strategies, this work focuses on the use of courtyards, a design strategy with roots dating back over 5000 years, being one of the oldest forms of vermacular architecture. While traditionally associated with Middle Eastern cultures, courtyards have been reinterpreted and adapted across diverse regions, including Latin America, China, and Europe. Despite their long history and unique role in shaping microclimates, the impact of courtyards in diverse aspects have been undervalued in more recent centuries. More specifically, the impact of courtyards as a passive design strategy is evident in both occupant comfort and building energy performance. As noted by [PERSON] [9], courtyards contribute significantly to creating a more comfortable living environment while also reducing energy consumption. In the specific context of Andalusia, courtyard architecture has played a prominent role in shaping traditional building design. As described by [PERSON] [10], Andalusian vermacular architecture is often characterized by a particular form where interior spaces of a building converge towards a central courtyard. This courtyard also serves as a private, outdoor space for social gatherings. The historical significance of courtyards, coupled with their demonstrable benefits in terms of comfort and energy efficiency, underscores their enduring relevance in contemporary architectural discourse. Their ability to create microclimates, promote social interaction, and enhance the overall liveability of buildings makes them a valuable design element for architects and urban planners seeking to create sustainable and human-centred environments.