esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Evaluation Setup
### Dataset Description  
To evaluate the classification performance of our proposed method, we use datasets that simulate the centre frequency offset and sample rate offset to train and test the method. In addition to the two major errors in the datasets, we also consider the damage in a variety of signal transmissions, including signal arrival times, nonimpulsive delay spread, Doppler offsets, and Gaussian thermal noise. Since these common time-varying random channel effects exist in most wireless systems, we include these effects in the datasets as much as possible.  
The dataset includes eight digital modulation methods, namely CPFSK, 4-PAM, GFSK, BPSK, QPSK, 8-PSK, 16-QAM, and 64-QAM. We extract the baseband signal of 128 sample points in the signal stream generated by the combination of each modulation mode, estimation error, and channel impairment. We divide signal captures into IQ two-way storage as with the signal representation described above. With each frequency offset, sample rate offset, and SNR combination, we get 500 test signal captures and 500 training signal captures. Each sample includes 3-20 symbols. The signal captures are generated at various SNRs between \(-10\) and 20 dB. To simulate different degrees of frequency offset and sample rate offset, we use the datasets listed in Table 1, ideal detectors without frequency and sample rate offsets (ideal), Dataset with various sample rate offsets (SRO 1 and SRO 2), detector with varying carrier frequency offset (CFO 1 and CFO 2). It is worth noting that the datasets listed in Table 1 are training sets. We train on training sets with different frequency and sample rate offsets to evaluate the models' tolerance to parameters estimation error.
## 2 Materials and Methods  
### Sampling Strategy and Preparation  
A suitable sampling strategy is essential to fulfill various criteria of reproducibility, value range, and variance within the sample. To meet these requirements, we have recorded a suitable dataset according to the American Society for Testing and Materials (ASTM) standard practice E1655-05 [41]. In the following, the mentioned criteria are described in detail. To develop a reliable calibration model, the characteristics of the samples included in this model have to correspond to the characteristics of samples analyzed in the future. Therefore, whole blood reperfusion of the porcine kidneys was performed before sample collection and the subsequent determination of the TWC. Another criterion was that the training dataset had to cover the range of TWC in kidney tissue during NMP. Here, the water content could vary due to osmotic imbalances caused by ischemia/reperfusion damage [17; 18]. To obtain representative values of TWC in non-physiological conditions, some kidney samples were additionally exposed to different drying times. Furthermore, the TWC may vary within the same kidney leading to deviations from the mean water content. Therefore, sub-sampling was performed to obtain a wide range of TWC values for the calibration model. For each kidney, samples were taken from six different regions in order to include possible spatial divergences.  
For this study, 23 kidneys from female German Landrace slaughterhouse pigs were used. The pigs' selection criteria were homogenous age (six months), weight (100-120 kg), and origin (local slaughterhouse with 10-15 pigs slaughtered per week) as well as health (inspected by an official veterinarian). Up to six kidneys per week were analyzed. A detailed description of blood and organ removal as well as storage can be found in [25].  
Each kidney was filled with temperature autologous whole blood (TBlood = 37 \(\pm\) 0.5 \({}^{\circ}\)C, hematocrit = 32 \(\pm\) 5%) and cut into six rectangular pieces with the respective dimensions of 3 cm \(\times\) 1 cm \(\times\) 0.5 cm (length \(\times\) width \(\times\) thickness), resulting in a weight of 2 \(\pm\) 0.05 g. The prepared tissue specimens were analyzed following the Association of Official Analytical Chemists (AOAC) standard method for moisture analysis 950.46 B, where the samples were dried at 102 \({}^{\circ}\)C for 18 h [42]. Generally, the TWC of porcine kidneys ranged from 81.5 to 89.3% (shown in Table 1). In order to predict this TWC range as good as possible for future applications during NMP, a majority of the samples of the training dataset was concentrated on this range. In addition, to reflect non-physiological conditions, the TWC was artificially reduced. Thus, some kidneys were also analyzed after 10 and 20 min of drying time.
# Inertial Sensors-Based Assessment of Human Breathing Pattern: A Systematic Literature Review
## 2 Methodology
### Search Strategy  
Three databases--Scopus, Web of Science (WoS), and PubMed--were selected for the conventional subject search based on the nature of the study topic area. Scopus is an abstract and indexing database developed by Elsevier Co. It claims to be the largest single abstract and indexing database ever built, indexing over 14,000 STM and social science titles from 4000 publishers. With 4600 health science titles, including complete coverage of MEDLINE, EMBASE, and Compendex, its vast repository comprises 27 million abstracts dating back to 1966, and it encompasses not only American journals but also papers from Europe and the Asia Pacific region, published in various languages, including English [44]. WoS is the world's oldest, most widely used, and authoritative database of research publications and citations, and it was established in 1964 by [PERSON]; its Core Collection database, based on the Science Citation Index, is a selective citation index that includes scientific and scholarly publications such as journals, proceedings, books, and data compilations [45]. PubMed is a free online research database for the health sciences maintained by the National Center for Biotechnology Information (NCBI) at the U.S. National Library of Medicine (NLM), which is part of the National Institutes of Health (NIH). Widely used by healthcare researchers, practitioners, and students around the globe, it contains over 35 million citations and abstracts of biomedical literature and offers links to full-text articles [46; 47]. The full search string definition for this study was derived from the terminology identified for the target population within the framework of the SLR using the PICOC framework (Table 2) and primarily focuses on the assessment of adult human BP using inertial wearable sensors. The search strategy was conducted until 4 May 2023 and involved leveraging the advanced search features of each database, employing two distinct search strings that targeted specific keywords in the titles and abstracts of the references. The main search string included the terms \"breathing pattern\", \"respiration pattern\", or \"respiratory pattern\" combined with \"assessment\", \"evaluation\", or \"analysis\" and was further combined with \"wearable\", \"inertial sensor\", or \"accelerometer\". On the other hand, the secondary search string encompassed \"rib cage motion\", \"chest wall motion\", \"chest motion\", \"rib cage mobility\", \"chest wall mobility\", or \"chest mobility\" coupled with \"assessment\", \"evaluation\", or \"analysis\" and was further combined with \"wearable\", \"inertial sensor\", or \"accelerometer\".
# Recent Developments and State of the Art in Flexible and Conformal Reconfigurable Antennas
## 2 Frequency-Reconfigurable Antennas
### Antennas at Millimetre-Wave  
The millimeter-wave (MMW) spectrum addresses the challenges of the demands for high bandwidth to cover high data rates and extends capacity to facilitate the services and applications for 5G. An adaptable and flexible antenna at wireless front-ends is required for the realization of 5G mm wave architectures [78]. As mentioned earlier, one of the challenges in designing flexible reconfigurable antennas is the choice of the substrate. This becomes more challenging in mmW because of the higher dielectric loss [55]. LCPs and polyethylene terephthalate (PET) which feature low loss-tangent, good flexibility, conformity, and surface adhesion are presented as great candidates for the substrates at higher frequency bands [79]. In addition, the inkjet-printing as a cost-effective, and precise fabrication method is a promising candidate due to its sensitivity of dimensions (very low wavelength) in mmW and its capability of fabricating of thin conducting layers. In Reference [78], a slotted T-shaped radiating patch embedded in a rectangular aperture cut inside the ground plane is inkjet-printed on a PET for operating frequency bands between 26.5-40 GHz. Four ideal switches, open/short metal contacts are employed in the middle of slots to generate the four modes of reconfigurability in this antenna. In Reference [79], a flexible frequency reconfigurable antenna in 20.7-36 GHz with four different switches configurations for 5G networks is presented. The antenna is placed on LCP and consists of a radiating fork shaped-patch and two stubs which are connected to the main patch by means of two PIN diodes. The inkjet printing for the conductive parts of the antenna which include the patch, stubs, and matched coplanar waveguide (CPW) feeding structure are employed. The efficiency of the antenna is reported to be in excess of 65% across the entire bandwidth. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Ref. **} & \multirow{2}{*}{**Antenna Type**} & \multirow{2}{*}{**Recomigurability Method**} & \multirow{2}{*}{**No of States**} & \multirow{2}{*}{**Used Materials**} & \multicolumn{2}{c}{**Freq. Gifts**} & \multicolumn{2}{c}{**Gain (4800)**} & \multicolumn{2}{c}{**Eff.
## 4 Assessment of Adjacent Building Damage with Ground Movements
### Building Damage Probability  
In order to perform a probabilistic analysis, it is necessary to determine the uncertainty of the entire process for computing the DPI. By understanding the uncertainty associated with the DPI model, an engineer can estimate the probability of excavation-induced building damage. To determine the uncertainty of the DPI model, the bias factor (BF) defined in the previous section is used for analysis.  
The BF is obtained by calculating the ratio of the DPI obtained from the proposed simplified calculation for ground movements in this study to the DPI obtained from the numerical analysis results. The BF is assigned a Gaussian distribution, as shown in Equation (14), and the mean (\(\mu\)) and variance (\(\sigma\)) are 1.0 and 0.24, respectively. Given the mean and variance of the DPI for a specific case, the probability density function of the BF can be determined.  
\[f(x)=\frac{1}{\sqrt{2\pi}\sigma}\exp(-\frac{(x-\mu)^{2}}{2\sigma^{2}}) \tag{14}\]  
Figure 15: Sagging and hogging damage patterns of buildings.  
By utilizing the probability density function of the bias factor (BF), it is possible to determine the probability of surpassing a specified DPI value. For example, the DPI obtained from the DPI model is 50, and the probability of exceeding 70 of the DPI obtained from the DPI model is the probability of exceeding 1.4 of the BF. The probability of exceeding 1.4 of BF can be determined through the probability density function of BF. To assess the probability of building damage resulting from an excavation supported by IVFRW, a simplified chart was developed, as depicted in Figure 16. The curves displayed in this chart were derived from repeated analyses of the aforementioned probability density function of BF for different deterministically computed DPI values, considering various specified damage levels.  
From the user's standpoint, the probability of sustaining building damage exceeding a specified level can be read from the chart with a calculated DPI value. For instance, if a deterministically computed DPI of 50 (sagging) is entered, the chart indicates a probability of exceeding moderate damage of 87% and a probability of exceeding severe damage of 22%.
## 2 Fuzzy Sets for Label Distribution
### Advantages of Fuzzy Label Distribution Learning  
Label distribution learning (LDL) [38] has been widely studied to deal with the overall label distribution. However, LDL does not fit some applications of the fuzzy concept, which is subjective and has no clear boundaries, such as complex emotions. Each instance may be associated with multiple emotions simultaneously. Each emotion is a fuzzy concept, is subjective, and has no clear boundaries. Fuzzy label distribution learning (FLDL) is a more appropriate model than probability distribution to describe the distribution of all labels of the instance.  
The two examples above describe the difference between them: (1) There is a video emotion recognition task shown in Figure 2, where the label set contains four basic emotions (angry, worried, sad, and happy). The value of each label is represented by a fuzzy membership degree; the higher the label value, the larger the membership degree. From the series of facial images in the video, we can see that the man has a complexity of emotions that contain several basic emotions simultaneously. He looks like he is very angry, not very worried, rather sad, and not happy. Therefore, the fuzzy membership degrees of these labels are 0.8, 0.2, 0.7, and 0.05, respectively. (2) There is a nature image classification task shown in Figure 3, where the label set contains four basic labels (sky, cloud, building, and water). The value of each label is proportional to the entire description of the instance. The natural scene image is formed by the sky, mostly cloudy, with some buildings, and lots of water. The proportions of these labels are 0.25, 0.45, 0.2, and 0.1, respectively. These labels sum up to 1.  
Figure 2: Fuzzy label distribution task.  
The task of FLDL can be transformed into the tasks of SLL and MLL. In SLL, the label of an instance corresponds to the maximum fuzzy membership degree. The predicted process of SLL can be formulated as follows:  
\[D(x^{\prime})\leftarrow\operatorname*{arg\,max}_{d_{j}}\{d_{j}(x^{\prime})\}_{j=1 }^{L}. \tag{3}\]  
In MLL, the label of an instance is the subset where the fuzzy membership degrees of instance to these labels are larger than \(\lambda\). The predicted process of MLL can be formulated as follows:  
\[D(x^{\prime})\leftarrow\operatorname*{arg\,}_{d_{j}}\{d_{j}|d_{j}(x^{\prime}) \geq\lambda\}_{j=1}^{L}. \tag{4}\]  
Examples of prediction labels of SLL, MLL, and FLDL are shown in Figure 4, where the triangle represents the corresponding label associated with an instance and the dotted line represents the constant \(\lambda\). Fuzzy membership degrees of an instance to all labels are computed. In SLL, the third label is the maximum fuzzy membership degree. In MLL, the first and third labels are larger than \(\lambda\). SLL and MLL can be regarded as special cases of FLDL. Therefore, FLDL is considered the more general model.  
The output space of FLDL is larger than SLL and MLL. There are \(L\) possible outputs for SLL, and \(2^{L}-1\) possible outputs for MLL. In FLDL, each label satisfies \(d_{j}\in[0,1]\). Therefore, there are infinite possible outputs for FLDL.
## 4 Challenges and Future Outlooks  
This article provides a thorough analysis of the fabrication of nano-modified coatings (self-healing-based nanocoatings, natural source-derived nanocoatings, metal/metallic oxide-incorporated nanocoatings, and carbon allotrope-based nanocoatings), as well as their performance in terms of corrosion mitigation. The practical difficulties in the emergence and fabrication of nanostructured coatings for preferred corrosion inhibition effectiveness include: (i) choosing the precise nanocoating substance that is particularly suited for a given substrate and operational surroundings (e.g., the flexibility of accumulation, which becomes quite difficult for a substrate with complicated geometries and variability in volume, adhesion on the substratum, etc.); (ii) choosing the proper fill/reinforcement; (iii) chemical impediment in the manufacture of nanocoatings; (iv) ability to be applied upon wide surface region; and (v) protracted effectiveness.  
In regard to improving the mechanical characteristics that might slow the dispersion of cracks in the covering, numerous underpinning pathways for interface reinforcement, including the engagement between NPs and the matrix, must be improved. This might lead to the creation of unique microstructures (for example, a refined scale lamellar framework). The weak tribological and mechanical capabilities of certain coating matrices, such as their low resilience to interface erosion and wear, might be effectively accounted for by NPs in this approach. Additionally, since the maximal packing capacity of NPs with a greater surface region is constrained, homogeneous dissemination of NPs in the matrix is essential to achieving superior mechanical characteristics (such as ductility and strength) of nanocomposite coverings. Moreover, competent preparatory and treatment techniques are required to produce excellent particle dispersion. Additionally, using methods like molecular assembly or atomic layer deposition (ALD), the interface between the NPs and the matrices could be accurately altered at the atomic or molecular scale to produce certain intriguing shapes, including core-shell hybrid NPs. Considering this, more research is still required to discover the mechanical characteristics of additional types of NPs using improved characterization approaches and established NP manufacturing processes. It is also important to provide statistical assessments of the mechanical characteristics of NPs in regard to size dependence and material influences, etc. Furthermore, it is crucial to have a greater understanding of the functions represented by NPs in the context of each application. It might, therefore, be highly beneficial to have direct visualizations of the interfacial behavior of NPs in operations at the nano, micro, and even atomic levels.
## 2 Related Works
This research formulates the issue as a multi-agent RL problem; they highlight the collaboration and learning policies in a multi-agent setting, but it does not provide specific details on the mechanisms or techniques employed for agents' cooperation. There are other recently developed approaches that deal with application-based multi-agent systems; [PERSON] et al. [35] focus on achieving robust bipartite tracking consensus in second-order multi-agent systems. The proposed approach aims to enhance robustness, reduce computational complexity, and simplify the control system's structure while managing uncertainties and disturbances in multi-agent systems. Furthermore, [PERSON] et al. [36] address a critical problem in the field of control systems, particularly in networked multi-agent systems. The primary goal of the proposed work is to analyze and improve the performance of containment tracking in networked agent systems that are affected by nonuniform communication delays. To summarize, RL has been widely applied in ITSs and in other application-based scenarios; certain research has shown significant advancements and realistic state assessments. However, benchmark traffic environments are still missing for fair comparisons among different RL algorithms. Additionally, there are very limited previous research findings related to agents' efficient cooperation in MARL, and the field lacks comprehensive studies focusing on MARL and efficient cooperation among agents. Despite the successful application of DRL for solving traffic and transportation issues, there is a current need for a well-structured network environment to enhance agents' learning capabilities. Our study, drawing inspiration from comparable studies [37], distinguishes itself by delving into the previously unexplored realm of multi-agent collaboration in-vehicle-navigation. The existing research has overlooked the need for holistic approaches to agents' cooperation, revealing a substantial gap in the existing literature. Therefore, our study represents the first attempt, to the best of our knowledge, to fill the void in the literature. Previous works have not presented efficient solutions for agents' cooperation, leaving the challenge unaddressed in the context of vehicle navigation. Note that our proposed objective is more flexible, as it can be used for any scenarios where multiple agents need efficient and privacy-preserved cooperation.
# Towards a Scalable Software Defined Network-on-Chip for Next Generation Cloud
## 5 Software Interface
### High-Level Programming Interface
#### 5.1.1 Data-Driven PXM  
PXMs specify the mechanism for synchronising threads. It is the process in which the memory is accessed and the threads are scheduled, created and destroyed on a target device. Unlike the von Neumann execution model, data-driven models require a private block of memory (_frame_) to store inputs used by the threads to run, a counter storing the number of inputs still not received (_scheduling slot_-SS), and the pointer of the thread body. The SS is decremented every time the consumer thread (here, we consider a thread as a sequence of operations atomically executed) receives a data from a producer thread. The triggering rule assumes that a thread is eligible for the execution once its SS counter is reduced to zero. In an explicit data-driven PXM, threads are allowed to explicitly schedule the execution of new threads, by adequately initialising their SS and passing the instruction pointer. We call threads adhering to this model data-driven threads (_DD-Threads_).  
Go implements concurrent execution through the _communicating sequential processes_ (CSP) mechanism. Such model is enabled by placing the _go_ keyword before the name of a function that must be executed concurrently. The function becomes a _goroutines_. By default, goroutines execute independently from the others, once their input list becomes available. Channels, special input buffers (i.e., typed storage elements), can be included in the goroutine input list. Channels can also be declared as buffered. It means that it is also possible to specify the number of available slots. In that case, a read operation on an empty slot leads the goroutine, waiting until the value becomes available (similarly, a write operation on a full buffer). Goroutines provide an implementation of the proposed DD-threading model.