esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Works  
Recently, interest in algorithms which allow optimal channel allocation in cellular networks has soared. Some such algorithms focus on minimizing the span of the channel used, while others focus on minimizing interference [8; 15]. In References [9; 12; 13; 16] the authors studied cellular systems using algorithms to allocate channels. According to [17], channel allocation methods include fixed, dynamic, and hybrid types. Different approaches for channel allocation were discussed in [18], such as a central approach, a distributed approach, a relaxed mutual approach, and a genetic algorithm approach. In [17; 18], a genetic algorithm was used to study channel allocation in wireless communication systems and to try and develop solutions to challenges arising from channel assignment [19; 20]. As a result,two strategies were developed: in the first \"Genetic Algorithm Lock\" (GAL), already assigned channels are locked when a call is on hold [21]; in the second \"Genetic Algorithm Switch\" (GAS), there is the possibility of switching a call to another channel during a connection phase [22]. Both of these studies noted the need to allocate the correct channel, as well as the challenges associated with assigning channels (especially in cellular networks) to new geographical settings so as to reduce the entire span of frequency, which is a function of demand and interference-free constraints, referred to as \"Channel Assignment Problem 1\" (CAP1), or to reduce interference, which depends on demand constraints, called \"Channel Assignment Problem 2\" (CAP2). However, it was observed in [23] that the used method, _Genetic Algorithm_ (GA), was unable to reduce the reuse frequency, even though channel allocation was carried out. By studying common allocation systems in mobile networks [24], reference [25] developed a unique centralized channel allocation which could effectively use scarce resource bandwidth. This model contains the so-called geographical model, which is essentially a group of contiguous, non-overlapping cells, each of which has a hexagonal form and which together form a parallelogram. The same study proposed a traffic model which mainly involves a dynamic channel allocation system in which there are no fixed channels. Hence, as soon as a call arrives, it is seen as a random process, and the function N (t) for all t \(\geq\) 0 and the interval (0, t) is used to decode the number of arriving calls. The procedure for determining the number of arriving calls N (t), t*0 is a Poisson procedure that has an average rate \(\lambda\). A modern algorithm technique, namely the \"Particle Swarm Optimization\" [26; 27; 28; 29], was adopted for channel allocation [30]. This technique depends on a worldwide optimization that depends on \"bird flocking\" social behavior. It has the features of both swarm-based and agent-based techniques, making it a rapid process, and its characteristics are different to those of common evolutionary algorithms (EA) such as those presented in [31]. The evolutionary algorithms mainly deal with channel allocation, and do not allow the minimization of frequency reuse distance.
## 4 Results and Analysis
### Restoration Results of Degraded Images on TDIRM
Each type of corruption is divided into three levels. As shown in Figure 20, both CAP and DehazeFormer exhibit certain degrees of contrast and tonal shifts, with a darker background. Restormer shows relatively limited effectiveness in removing dense fog. In comparison with other models, TDIRM not only maintains the fidelity of degraded images but also effectively restores color and texture information in traffic scenes, thereby enhancing overall image quality. Notably, TDIRM can eliminate most fog occlusion while preserving original content and details across varying fog intensities. It demonstrates particularly excellent restoration performance for moderate and light fog, where it nearly completely removes fog effects. However, its performance remains somewhat limited for dense fog. This limitation primarily stems from: the model was trained on synthetic fog-clear image pairs, where the data distribution differs from real-world fog images. And the performance gap becomes more pronounced as fog severity increases. Furthermore, due to the characteristics of traffic degradation images, in upper image regions (sky areas), dense fog almost completely obscures objects. This makes complete fog removal particularly challenging in these areas. Although the model cannot completely eliminate fog interference, it is still capable of clearly restoring most traffic elements. Vehicle features that were difficult to distinguish due to fog occlusion and small target vehicles that were originally overlooked in the distance reappear after restoration.
## 4 Numerical Results
### Complexity Comparison  
Figure 7 shows the number of multiplications required by the proposed SAOR and CGA-SAOR methods, as well as the conventional AOR and SOR methods, as a function of the number of user terminals. We use a higher number of users, i.e., 64, which allows for a more comprehensive comparison across a broader range of system sizes. The iterative methods visibly demonstrate a significant reduction in computational complexity compared to the exact MMSE solution, especially in large-scale configurations where the number of users increased, as can be seen in the figure. Among all detectors, the SOR detector exhibits the lowest complexity. Further, it can be observed that the complexity of the proposed CGA-SAOR method is slightly higher compared to that of the proposed SAOR and conventional AOR detectors. Although the proposed CGA-SAOR algorithm needs a negligibly higher number of computations than the other iterative detectors, we have shown that the CGA-SAOR detection algorithm outperforms the aforementioned algorithms. Moreover, the proposed CGA-SAOR algorithm achieves the desired performance with fewer iterations, while the other iterative detectors need more iterations to reach a similar level of performance, which has been verified through numerical results; hence, they do not converge rapidly. This means that the computations of the CGA-SAOR algorithm can be reduced further. Thus, the proposed CGA-SAOR detection algorithm offers a good trade-off between complexity and error performance compared to the iterative detectors discussed.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Algorithm** & **256 \(\times\) 64** & **256 \(\times\) 16** \\ \hline Linear MMSE & 34.6376 & 9.4434 \\ AOR & 31.4155 & 6.8820 \\ SOR & 26.4270 & 6.1127 \\ Proposed CGA-SAOR & 32.4541 & 7.0266 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Mean evaluation time of different algorithms.  
Figure 6: SER performance comparison of the proposed algorithm with the linear MMSE method in an \(N\times K=256\times 32\) MIMO system using 64-QAM modulation under both imperfect and perfect CSI conditions.  
In summary, the simulation results indicate that, in comparison to the exact MMSE detector, the proposed CGA-SAOR detector exhibits virtually the same error rate with significantly lower computational complexity. Additionally, it demonstrates comparable complexity and notably improved performance when compared to recently reported iterative detection methods across all considered scenarios, even in systems with a relatively higher number of user terminals in relation to BS antennas. This implies that the BS will be able to accommodate the rapidly increasing number of users simultaneously.
## 5 Conclusions  
Ionospheric irregularities and scintillations and their associated motions in the Taiwan-Philippine sector have been observed and specified by the FS7/COSMIC2 data, the VIPIR located at Hualien, Taiwan, 133 ground-based GPS receivers located in Taiwan and the surrounding islands, and two high-sampling, software-defined GPS receivers. The integrated system has the potential to provide scintillation intensities, zonal drift measurements, and even three-dimensional irregularity structures. We also suggest that a post-sunset decrement of the virtual heights of fixed-frequency ionospheric echoes could be a good precursor for post-sunset scintillation and ESF events. In the future, we expect to identify the ionospheric conditions in the Taiwan-Philippines sector that led to the onset of plasma/R-T instabilities and to forecast the growth and the timing/duration of each instability. An examination of these instabilities will form the basis for the forecast of the timing and severity of (GNSS) radio scintillations.  
Conceptualization, L.-C.T., S.-Y.S. and C.-H.L.; Data curation, L.-C.T., J.-X.L. and T.B.; Formal analysis, L.-C.T.; Funding acquisition, L.-C.T. and S.-Y.S.; Investigation, L.-C.T., J.-X.L. and T.B.; Methodology, L.-C.T., S.-Y.S. and C.-H.L.; Project administration, L.-C.T. and S.-Y.S.; Resources, L.-C.T., J.-X.L. and T.B.; Software, L.-C.T. and J.-X.L.; Supervision, L.-C.T., S.-Y.S. and C.-H.L.; Validation, L.-C.T.; Visualization, L.-C.T.; Writing--original draft, L.-C.T.; Writing--review & editing, S.-Y.S., T.B. and C.-H.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Office of Naval Research, U.S.A. [grant number N00014-21-1-2486] and in part by the Ministry of Science and Technology, Taiwan [grant number MOST 110-2119-M-008-003].  
Figure 7: Time variations of virtual ranges (referring to the left \(y\)-axis) of ionospheric echoes at different sounding frequencies _fs_ of 1.72 (dark blue), 3.08 (light blue), 4.72 (green), 5.65 (orange), and 7.15 MHz (red) from the Hualien ionograms recorded on 26 October 2021. Another temporal profile of scaled _fol_ is also shown and referring to the right text. Note that the spread-F features were happened between 13:19–15:04 UT, i.e., 21:19–2304 LT in Taiwan.  
Data Availability Statement: The FS7/COSMIC2 \"ionPhs\" data can be downloaded from the COSMIC Data Analysis and Archive Center (CDAAC, [[http://cdaac.www.cosmic.ucar.edu/](http://cdaac.www.cosmic.ucar.edu/)]([http://cdaac.www.cosmic.ucar.edu/](http://cdaac.www.cosmic.ucar.edu/)) (accessed on 1 December 2021)) and the Taiwan Analysis Center for COSMIC (TACC, [[http://tacc.cwb.gov.tw/cdaac/](http://tacc.cwb.gov.tw/cdaac/)]([http://tacc.cwb.gov.tw/cdaac/](http://tacc.cwb.gov.tw/cdaac/)) (accessed on 1 December 2021)).  
The authors would also like to thank UCAR's CDAAC, NSPO Satellite Operations Control Center (SOCC), and the CWB, Taiwan, for providing FS7/COSMIC2 data and GPS data.  
The authors declare no conflict of interest.
## 2 Background
### Test Case Optimization
#### 2.2.3 Test Case Prioritization  
Prioritization changes the test case execution order to maximize early fault detection and minimize cost. When creating a new test suite \(T^{\prime}\) from cases in an existing test suite \(T\), the objective function described above is used to optimize the execution order of \(T\). Unlike test case selection, the objective is achieved by changing test case execution priority rather than finding a minimum number of test cases. For example, if one test case finds more faults than any other case, it is run first. We use test case prioritization for regression testing.
## 5 Application: IoT Temperature Time Series
### Data Description  
We analyze data spanning the time period from 1 October 2020 to 25 February 2021 on a sample of \(N=50\) sensors that are distributed across the contiguous US, with a concentration in the Upper Midwest, Southeast, and the East coast. A sensor measures the internal room temperature at 15 min intervals. Figure 1 shows an example of a sensor's temperature stream and when an alert was sent (the alert time is denoted by the vertical black line). We see that, just prior to the alert, internal temperatures plunge, while the external temperature is at a low level.  
In order for this program to be effective, it is imperative that there is accurate information on whether a customer actually takes meaningful action in a timely manner after receiving an alert in order to avoid freeze loss. Although ideally, this information would be directly obtained from the customer, this is rarely possible in practice. As such, the effectiveness of the alert must be ascertained purely based on the observed pre-alert and post-alert time series. We refer to this analysis as intervention impact analysis. Essentially, a customer action has likely occurred if there is a large increase in post-alert temperatures that are incongruuous with forecasts generated by a suitable time series model trained on pre-alert temperatures.  
Since many of the IoT temperature time series exhibit non-linear behavior, methods such as Bayesian structural time series are inadequate for intervention impact analysis. Alternatively, the NARX and RLARX models are capable of learning non-linear behavior directly from the data, thus, we will use them in substitution of BSTS models for our impact analysis. Performance will be compared to both GP-NARX and GP-RLARX models.
## 4 Discussion
Our own results confirm the correlations between these spray liquid parameters and treatment efficacy. Multifunctional adjuvants EXP 1, 2, and 3, were similar to AtB due to surfactant content, largely the retention of droplets and coverage of the plant surface, by reducing the ST and CA [25; 26]. The presence of penetrants, which include plant oils, increases the penetration of herbicides through plant tissue [27]. For electrical conductivity (EC), the FEM 1 and FEM 2 alone or with adjuvant EXP 1, EXP 2, and EXP 3 solutions did not stand out in comparison to AtB and S. The water with adjuvant solutions presented lower capacity in this variable compared to FEM solutions. According to [28], it might have to do with the hydrogen ion potential because the solution with the highest EC presented a lower pH. Our own study results do not confirm such a correlation, as a low pH also corresponded to a low EC. The addition of adjuvants to water did not increase the EC as much as adjuvants with FEM 2. Solutions containing FEM 2 with adjuvants, especially EXP 1, EXP 2, and EXP 3, presented higher EC than water with adjuvants. In some opinions [29], adjuvants could change the electrical values of the spray solution. A higher EC is beneficial because of a direct influence on larger deposition on the surface, and finally better biological effectiveness [30]. Sugar beet is unable to compete with weeds, especially in the first weeks after sowing. The greatest threat to sugar beet is the presence of _Chemopodium album_, _Amaranthus retroflexus_, _Galium aparine_, and volunteer _Brassica napus_, as well as _Echinochla crus-galli_[31; 32; 33]. Some studies [34] indicate that herbicides could greatly control weeds, especially when they are applied with adjuvants that promote activity of their active substances. The herbicide's arrival at its site of action in the weed is preceded by the preparation of the spray liquid, application, retention, absorption, and transport to the site of action. On this pathway, herbicides face many obstacles, which adjuvants should overcome. Single-component adjuvants can limit only some factors, so the use of multicomponent and multifunctional adjuvants is essential [35]. The results indicate that appropriately selected multicomponent adjuvants increase herbicide efficacy [36]; the results of our own research confirm these reports. The EXP multicomponent adjuvants improved the effectiveness of the used herbicides. The effect of the tested adjuvants was similar to or often better than standard adjuvants based on methyl esters, surfactants, and pH buffers.
# Smart Cities of the Future as Cyber Physical Systems: Challenges and Enabling Technologies
## 5 A Holistic Approach toward Next Generation Smart Cities Application Management
### IoT Applications Management  
IoT application management may take place based on an end-to-end orchestration platform able to manage the deployment and management of IoT workloads over the available edge and cloud computing infrastructure, considering the set of requirements per IoT application and the interplay between cloud and edge resources. A set of existing open-source orchestration frameworks can be considered (e.g., KubeEdge, and K3S). We will refer to the main modules and functionalities that have to be supported. These modules concern the IoT applications manager, the cloud/edge resource managers, and the IoT device managers.  
The IoT applications manager has the main responsibility for tackling the operational lifecycle of cloud-native IoT-containerized applications. This includes modules for managing application components deployed at the cloud and edge part of the infrastructure considering the interplay between cloud and edge resources in terms of the resource usage efficiency and performance aspects, as well as managing the IoT devices interconnected at the edge components. The IoT applications manager has a view on the allocated resources for serving each application graph.  
This produces a deployment plan that is realized over the materialized network slice in case of a deployment over 5G infrastructure or over the available compute clusters in the case of a pure cloud/edge computing infrastructure. The deployment plan guides the instantiation of the containerized application components at the cloud and edge computing part of the infrastructure. The IoT applications manager interacts with the resource manager at the cloud and edge part of the infrastructure.  
The resource managers are responsible for managing the deployment part at the edge/cloud part of the infrastructure, the provision of continuous feedback regarding the status of the edge/cloud application components (e.g., health checks, failures, and self-healing reporting), and the related consumption of resources. Each resource manager is able to handle events related to computer offloading and mobility aspects, taking advantage of the activated IoT functions. They are able to manage various edge/cloud clusters, considering the deployment needs of each application and the available VIMs and/or computing clusters.  
IoT device managers are also provided for synchronizing the device updates from the cloud to the edge node and vice versa and scheduling actions on the IoT device, considering the supported APIs and the exposed functionalities per intelligent IoT device. Through a continuous interaction among the main application manager, the various resource managers and the IoT device managers, end-to-end orchestration of IoT applications over programmable infrastructure is taking place.
# An Unsupervised Behavioral Modeling and Alerting System Based on Passive Sensing for Elderly Care
## 3 Sensor Setting
### Sensor Network Deployment  
The deployment procedure of our AAL system involves the installation of the selected sensor kit and a home WiFi router. Larger or multi-level flats may require the use of an off the shelf wireless range extender, which does not change the overall network architecture. Figure 1 shows a typical installation setup. The WiFi router is placed in the center of the house, to achieve optimal signal coverage for all sensors. The motion sensor is installed at 'a point of passage' of the house, which typically could be at the main corridor area. The door sensor is deployed at the main entrance of the house. A full deployment typically requires 45 to 60 min, as measured in the pilot installations.  
All devices feature the same micro-controller and network processor, a CC3220 System on Chip (SoC) by Texas Instruments (Dallas, TX, USA). This unit is a Wi-Fi certified product, providing IoT networking security, device identity and keys and is optimized for low-power operation. The certified stack implements both IPv4 and IPv6 protocols, with industry-standard, optimized BSD sockets (both TCP and UDP), secured by SSL/TLS.
###### Abstract  
This article studies the underwater acoustic (UWA) communications associated with multiple input-multiple output (MIMO), single carrier with frequency-domain equalization (SC-FDE), and with low-density parity-check (LDPC) codes. Low-complexity receivers such as equal gain combining (EGC), maximum ratio combining (MRC), and iterative block--decision feedback equalization (IB-DFE) are studied in the above-described scenarios. Furthermore, due to the low carrier frequencies utilized in UWA communications, the performance of the proposed MIMO scenarios is studied at different levels of channel correlation between antennas. This article shows that the combined schemes tend to achieve good performances while presenting low complexity, even in scenarios with channel correlation between antennas.  
underwater communications; LDPC; MIMO; SC-FDE +
Footnote †: journal: Applied  
00 2022 by the authors.  
Liconse MDPI, Basel, Switzerland.  
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).
## 4 Discussion
In light of both the preceding research outcomes [27; 32] and our own findings, it is advisable for clinicians to conduct supplementary clinical evaluations pertaining to the flexibility and strength of the hip flexors. This is essential in order to ascertain whether suboptimal performance on the mSEBT, particularly with regard to deficits in the ANT reach direction, is associated with challenges in achieving increased hip extension, which may be indicative of muscle tightness, or with limitations in providing adequate trunk support. Our study further emphasized the impact of flexibility, as assessed through the Thomas test, on reach distances. The observed negative correlation between hip flexor flexibility and ANT reach distance indicates that reduced flexibility in the hip flexors may pose obstacles to achieving optimal reach distances in this specific direction. This finding holds particular relevance for clinicians and trainers who are involved with individuals necessitating precise control of anterior reach movements, underscoring the importance of striking a balance between hip flexor flexibility and strength. It suggests the need for interventions that optimize both aspects for particular reach movements. Regarding the sit-and-reach test, which exhibited only weak correlations with reach distances in the mSEBT, its limited role in predicting reach performance suggests that alternative measures, such as the Thomas test or specific strength training, maybe more relevant for clinical assessments of balance and reach capabilities. Some limitations necessitate acknowledgment in the current study. Firstly, the study exclusively encompassed healthy individuals, thus warranting future investigations to explore whether the notable correlations between strength, flexibility, and mSEBT performance would hold true among individuals who have incurred injuries leading to joint stiffness. Secondly, the prospective impacts of trunk movements on hip muscle strength necessitate examination in subsequent studies, as the trunk and hip exhibit interconnected kinematic relationships. Lastly, it is essential to highlight that the study's sample consisted exclusively of females. Consequently, future research endeavors should involve male participants to facilitate a comprehensive exploration of potential distinctions in trunk and hip mechanics during mSEBT performance.