esa-sceva/satcom-chunk-collection huggingface dataset:
# A Joint Denoising Learning Model for Weight Update Space-Time Diversity Method +
## 6 Conclusions  
This paper proposed an improved STD structure with a joint noise-reduction learning model to process UWA data with complex-type characteristics for noise-reduction, suppress MAI, and improve communication quality. Specifically, a staged model was constructed to minimize the effect of nonlinear noise during STD processing. The model pre-processes the UWA data by complex-type orthogonal compression in the first stage. Then, the CGAN is adapted to become a complex-CGAN dedicated to UWA data processing so that the  
Figure 23: The comparison of constellation diagrams of different methods: (**a**) the constellation diagrams for “Wiener+Ours2”; (**b**) the constellation diagrams for “KBM-ANC+Ours2”; (**c**) the constellation diagrams for “CNN+Ours2”; (**d**) the constellation diagrams for the proposed joint processing method.  
learning model can perform noise-reduction for complex-type data. In the second phase, an improved STD structure is constructed that constrains the main diameter and other interference paths through weight updates. It can effectively suppress MAI that came with the use of CDMA as a transmitted signal.  
The proposed method effectively solves the UWA communication problem in a nonlinear noisy environment and effectively improves handling complex-type data while suppressing MAI. However, the UWA channel is a changing structure, and the proposed method does not adapt to the more drastic channel changes. It is worth investigating how to maintain the advantages of STD processing with the complexity of channel changes in future work. For example, it can be explored to add the extraction of channel features in the pre-processing stage and dig into the channel's variation characteristics as much as possible during the network model construction. For the STD structure, we can also introduce the space-time coding technique to train the receiving process by rule coding to improve the communication performance of the sender and receiver.  
Conceptualization, Y.Z. and P.J.; data curation, Y.Z.; formal analysis, Y.Z.; funding acquisition, P.J.; investigation, Y.Z.; methodology, Y.Z. and Z.H.; project administration, P.J.; resources, P.J.; software, D.Z.; validation, Y.Z. and D.Z.; visualization, Y.Z.; writing--original draft, Y.Z.; writing--review and editing, D.Z. and P.J. All authors have read and agreed to the published version of the manuscript. This research was funded by the National Key Research and Development Program of China (2020 YFB0505803) and the National Key Research and Development Program of China (2016 YFB0501800).  
Not applicable.  
The authors declare no conflict of interest.
# A Differential Measurement System for Surface Topography Based on a Modular Design
## 5 Experimental Verification
### D Topography Measurement  
A coin was scanned using the proposed system, with a scan range of 2 mm \(\times\) 2 mm and a spacing of 10 \(\upmu\)m. The experimental setup and the scanned image are shown in Figure 10.  
In order to quantitatively evaluate the topography measurement performance, a set of roughness comparators was measured. Table 2 shows the comparison of the roughness parameters measured by the proposed system, namely, the HQU and a reference system, Mahr XR20. Each measurement was repeated five times. The average values are listed in Table 2. The standard deviation of each measurement was within 0.005 \(\upmu\)m.  
It was shown that the proposed system was able to achieve nanometer accuracy when measuring surface roughness.  
Figure 10: Experimental setup and coin scanning results.
## 4 Experiment
### Leaf Retrieval Experiment  
In this section, in order to evaluate the feature discriminative ability of our method further, several experiments are carried out to compute the leaf retrieval results, where the leave-one-out test scheme is applied. Suppose that there are \(N\) samples and \(K\) categories in a leaf dataset and let \(X_{ki}\) be the \(i\)-th leaf image that belongs to class \(k\) with \(C_{k}\) samples. Firstly, we compute the Euclidean distances between \(X_{ki}\) and the other \(N\)-1 leaf images. Secondly, the average precision [61] for \(X_{ki}\) is formulated as follows:  
\[AP(X_{ki})=\frac{\sum_{n=1}^{N-1}(P(n)\times s(n))}{C_{k}-1} \tag{11}\]  
where \(P(n)\) means the precision at cut-off \(n\); \(s(n)\) is equivalent to 1 if the \(n\)-th retrieval image is relevant to \(X_{ki}\) and 0 otherwise. Finally, the retrieval evaluation metric mean average precision (MAP) can be obtained via the following equation:  
\[MAP=\frac{1}{N}\sum_{k=1}^{K}\sum_{i=1}^{C_{k}}AP(X_{ki}) \tag{12}\]  
Without loss of generality, two simple leaf datasets (Swedish, Flava) and two complicated datasets (Leafsnap, Turkey Plant) are used in our retrieval experiments. The retrieval MAP results of our method are compared with those of the newly published state-of-the-art approach IMTD+relu5_2 [38]. One can see from Figure 14 that our method gets higher MAP scores on Swedish and Turkey Plant than IMTD+relu5_2. The MAP scores of our method on Leafsnap is 49.16%, which is very close to the 49.44% of IMTD+relu5_2.  
We randomly select five leaf images from Flavia dataset and display the top 10 retrieval results for each leaf image. It can be seen from line 2 of Figure 15 that there is only one wrong retrieval result that shares similar appearance with the query image. What is more, we also display the top 10 retrieval results for five leaf images from the Leafsnap dataset. The closest retrieval images for all the five queries are correct, which is consistent with the identification results in Table 9. All the 10 retrieval results are correct for the query images in lines 3 and 5 of Figure 16; this can prove the feature representation ability of our method. Although there are several wrong retrieval results in lines 1, 2 and 4 of Figure 16, the wrong retrieval leaves have visual appearance and shape very similar to the query images, especially for the results in lines 1 and 4.  
Figure 13: Confusion matrix result of our method on Turkey Plant dataset.
# Avoiding Obstacles via Missile Real-Time Inference by Reinforcement Learning
## 5 Conclusions  
This paper presents novel missile guidance laws using reinforcement learning. Design processes of guidance laws are explained in detail in terms of neural network architecture, reward function selection, and train method. The proposed guidance laws are focused on two scenarios. For scenario A, two-dimensional obstacle avoidance, the guidance law is designed in the way to avoid planar obstacles until it reaches the target. It avoids most obstacles by real-time inference of trained networks with limited information compared to existing algorithms with similar purposes. Meanwhile, failure can be predicted through critic network which is naturally generated during the learning process. Thus, it allows the missile to take action before a missile makes a fatal disaster, such as hitting friendly ships. For the 3D terrain avoidance, which is scenario B, a missile guidance law based on RL is designed to overcome terrain features through real-time inference. It keeps its altitude low to ensure it is not seen by radar on the top of the field while striking the target.  
In summary, the proposed RL-based missile guidance laws are not only able to strike the targets while avoiding obstacles and topographic features with limited information, but also able to determine the probability of the success rate, i.e., whether the mission is achievable or not. Numerical simulations show their effectiveness with some inherent limitations.  
Conceptualization, methodology, and WritingOriginal draft preparation, D.H.; validation, S.P. All authors have read and agreed to the published version of the manuscript. This work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIT) (NRF-2021R1F1A1063780). Not applicable. Not applicable.  
Figure 19: Critic value of the episode in Figure 11.  
Figure 20: Failed trajectory for scenario B.
# Data Analysis, Simulation and Visualization for Environmentally Safe Maritime Data
###### Abstract  
Marine transportation in Aegean Sea, a part of the Mediterranean Sea that serves as gateway between three continents has recently seen a significant increase. Despite the commercial benefits to the region, there are certain issues related to the preservation of the local ecosystem and safety. This danger is further deteriorated by the absence of regulations on allowed waterways. Marine accidents could cause a major ecological disaster in the area and pose big socio-economic impacts in Greece. Monitoring marine traffic data is of major importance and one of the primary goals of the current research. Real-time monitoring and alerting can be extremely useful to local authorities, companies, NGO's and the public in general. Apart from real-time applications, the knowledge discovery from historical data is also significant. Towards this direction, a data analysis and simulation framework for maritime data has been designed and developed. The framework analyzes historical data about ships and area conditions, of varying time and space granularity, measures critical parameters that could influence the levels of hazard in certain regions and clusters such data according to their similarity. Upon this unsupervised step, the degree of hazard is estimated and along with other important parameters is fed into a special type of Bayesian network, in order to infer on future situations, thus, simulating future data based on past conditions. Another innovative aspect of this work is the modeling of shipping traffic as a social network, whose analysis could provide useful and informative visualizations. The use of such a system is particularly beneficial for multiple stakeholders, such as the port authorities, the ministry of Mercantile Marine, etc. mainly due to the fact that specific policy options can be evaluated and re-designed based on feedback from our framework.  
m pacs: **Keywords:** maritime transportation; data engineering; modeling and simulation; Bayesian networks; social network analysis +
Footnote †: journal:
# Diurnal Cycle of Passive Microwave Brightness Temperatures over Land at a Global Scale
## 4 Results and Discussion
### Spatial Distributions of Global Land Diurnal Brightness Temperature Range
The rest of the land cover types with more vegetation and higher moisture content generally show a rather lower DTR. This is in line with previous studies [6; 7] that reported the effect moisture and vegetation on the TB DTR. In terms of seasonal comparison, higher DTR amplitudes are evident during January (e.g., Northern Hemispheric winter) than in July (e.g., Northern Hemispheric summer) at 89 GHz expect for desert regions. As the microwave frequency becomes lower to 89 GHz (e.g., 37 GHz, 23 GHz, and 19 GHz), the behavior of seasonal variations in DTR varies for different land cover types with less consistency. Figure 10 illustrates a comparison between monthly DTR from the initial GMI diurnal cycle and from the final half-hourly TB product for June 2016 at 23 GHz with vertical  
Figure 9: Mean monthly DTR averaged over different land cover types for two contrasting seasons of January 2016 and July 2016 for the (**a,c,e,g**) Northern Hemisphere, and the (**b,d,f,h**) Southern Hemisphere at (**a,b**) 19 GHz vertical polarization, (**c,d**) 23 GHz vertical polarization, (**e,f**) 37 GHz vertical polarization, and (**g,h**) 89 GHz vertical polarization. polarization. It can be seen that the value of GMI DTR in each pixel is almost the same as the DTR derived by the final TB product over the GPM coverage region (e.g., between 68\({}^{\circ}\)S and 68\({}^{\circ}\)N). The reason behind this agreement is that the GMI diurnal cycle has been herein moved with respect to SSMIS/AMSR2 daily data with preserving the shape and amplitude of the diurnal cycle. The only improvement from the final half-hourly TBs is that one can obtain DTR maps for the polar regions as well while maintaining consistency.
# Combined Use of 3D and HSI for the Classification of Printed Circuit Board Components
## 3 Results
This is due to geometrical similarities shown in Figure 15.  
The smaller the objects are, the more important it is to use 3D sensors that allow a high-resolution detection so that even the smallest objects can still be captured in detail. In this context, it is also important to mention that the sensors chosen for data acquisition should always be selected in accordance with the application. Capturing small objects with a low-resolution 3D scanner will not give satisfactory results. This is also reflected in the statistics computed for each class shown in Table 1. With decreasing object size, an exact classification becomes more difficult. For the calculation of the statistics, the underlying dataset was manually annotated and used for the computation of the confusion matrix. Figure 13: Result of classification for PCBs using spectral features (after step 2). Classification of three board types and four different types of components on board (PS, ecob 1, ecob 2 and ecob 3). The overall statistics for the entire dataset are shown in Table 2. It can be stated that with an Overall Accuracy (OA) of 98.24%, a satisfactory result is achieved. Figure 14: Result of classification for PCBs using 3D features (after step 3). Classification of four different types of components on board (ecob 4, ecob 5, ecob 6 and ecob 7). Figure 15: Misclassification due to geometric similarities in the point cloud. For comparison purposes, a SVM based classification (C-SVC, RBF-Kernel, C: 2048, gamma: 2.8284), which is one of the most widely used pixel-wise classifiers, was additionally performed. The SVM method was implemented in MATLAB using the LIBSVM library [35]. The optimization of the hyperparameters was determined by a 10-fold cross-validation. For the training data set, 10% of samples of each class were randomly selected. The main purpose of the comparison is to show, that an approach, combining 3D and HSI, has advantages in terms of classification. The results of the SVM classification are also shown in Figure 10:0 Considering the statistics, it is clearly evident that a classification processing 3D and HSI data outperforms a SVM classification based only on HSI.
### Post-Shannon Communications for IoT  
The classical information theory was established by [PERSON] in [17], where three levels of communications, including technical (reliable symbol transmission), semantic (message's meaning transfer) and effectiveness (achieve goal/pragmatic aspect of message exchange) problems were defined. [PERSON], in [17], considered solely the technical problem, which focuses on the _accurate_ transmission of symbols. However, several applications for emerging sixth-generation (6G) or future-generation (XG) wireless communications/networking systems in the context of IoT demand to deal with the semantic and effectiveness aspects of the message. In fact, future XG systems fold the semantic of message and the goal of message communication into their design. This is required in these applications in order to fulfill certain performance features, including sustainability (robustness), latency, reliability, security, etc. Studying these new aspects of the message goes beyond the conventional Shannon paradigm/framework, and are referred to as post-Shannon communications (PSCs) [18]. For example, in goal/task-oriented communications [19], the success of execution for specific task (effectiveness problem) at the destination/receiver is the key concern, and is demanded by the transmitter.  
In particular, a first discussion of the PSC for 6G can be found in [18]. The use of PSC for MC is studied in [20], in which the possible capabilities of MC for 6G is discussed for the first time. Also, a detailed discussion of the requirements for _tactile internet_ (which refers to the data transfer in real-time (extremely low latency) in combination with high availability and reliability requirements) and 6G can be found in [21], in which the PSC is introduced to be of particular importance for several key areas of applications for 6G, wherein new communication scenarios, performance requirements and open questions for the PSC are discussed as well. Moreover, the wireless communication systems in 5G and beyond networks, which include reconfigurable intelligent surfaces (RISs) [22], deal with aspects such as localization, synchronization and beamforming design. These aspects in RISs often require use of the semantic metrics rather than the conventional Shannon metrics; cf. [23; 24] for further details. Moreover, various applications in the context of smart medical and health-care systems for 6G networks require task accomplishment [20], and are needed to adapt the encoded signal depending to the specific application-driven requirements of the receiver.
## 3 Architectural Solution
In general, to monitor a particular area we have many devices, also with different technologies, and the central system has to process a huge amount of data from different devices and, sometimes, from different networks. In brief, there is an integration problem between physical and application layer. In these cases, it is necessary to devote particular care to successfully ensure _data interchange_ and _technology integration_[14] with the use of an appropriate _middleware_. However, the matter is not primarily limited to technological integration. In fact, the availability of a powerful and reliable integration mechanism is crucial for the management of big amount of information received by the devices, to clean the data affected by errors and to filter them according to precise rules. Last but not least, the middleware becomes essential to make data available (dispatching) to the software applications in a simple and reliable way by favoring the integration towards the upper level [15; 16; 17]. The middleware layer must assure flexibility and scalability, and must support the processes of integration by reducing costs and improving the effectiveness of the business processes. WhereX [18] is the solution we adopted as middlware layer. WhereX is an innovative _event-based_[19] middleware made by the Wireless and RFID laboratory of the University of Messina. WhereX is able to assure an effective technological abstraction, switching from a vision linked to specific interfacing issues (Sensor Network, GPS, WiFi, RFID device, ZigBee, _etc._) to the management of the events generated by each device. This approach allows to integrate the management logic of different devices. Furthermore, it includes a rule engine that can detect whether a specific condition holds, appropriately reacting by filtering, and potentially cleaning, all the events generated by the devices. Finally, the middleware is equipped with a very flexible and scalable software architecture based on SOA [20; 21] and the XMPP communication protocol. All these characteristics ensure an appropriate guarantee towards future technological evolutions. On the top of the reference architecture is the _application layer_ that handles the data acquired by sensors (the measured values) and allows the manager to analyze the data of energy consumption both in _real-time_ and in _historical mode_.
## 4 Discussion  
The aim of this study was to investigate whether a single-case experimental design (SCED) approach, taking into account personal preferences, an uncontrolled environment, the stage of dementia, and the heterogeneity of the population, is applicable to investigate differences in light consumption for people with dementia in a real-life situation, such as a home environment. In this first attempt to do so, the lessons learned should also provide recommendations to develop a suitable light protocol to study light interventions in the homes of people with dementia.  
A subsequent question was whether people with dementia living at home would be exposed to substantially brighter light and light of a higher correlated color temperature when they used an off-the-shelf dynamic lighting system in their home compared with regular lighting. To gain more insight on its effectiveness, we also explored at what time of day the contribution of the lighting system was most pronounced (moming, afternoon, or evening) and whether the effectiveness was moderated by season. Finally, user experiences were evaluated, as their (positive) experiences ultimately determine the usefulness of the lighting solution.  
It seems that the used real-life longitudinal single-case experimental design (SCED) is applicable to studying a lighting system suitable for people with dementia living at home, despite the uncontrolled environment, stage of dementia, and heterogeneity of the study population. In addition, this study approach is sufficiently sensitive for demonstrating differences in the presentation of light during the day after an exposure phase of 4 weeks in light intensity and color.  
In the following, we first discuss the results on the light intervention. After this, we reflect on the study design, including the protocol, data collection, and analyses, by discussing five reasons why the used study design is applicable and meaningful for application to this heterogeneous population. Lastly, recommendations, based on lessons learned, for future research are made.