esa-sceva/satcom-chunk-collection huggingface dataset:
# Smart Meter Gateways: Options for a BSI-Compliant Integration of Energy Management Systems
### Smart Metering in Germany  
Putting a high emphasis on standardization and security, the German smart metering approach is based on two major components: _Smart Meters_, and _Smart Meter Gateways_ (SMGWs), whereby the combination of both is referred to as _Smart Metering System_ (\"Intelligentes Messsystem\") [6; 7]. The _Smart Meter_ itself is a digital power meter capable of providing temporal fine-grained power measurements, and has a display for visualizing energy values for different time frames. The SMGW is a communication device with two major functionalities. It can automatically communicate measurements from connected _Smart Meters_ to _External Market Participants_ (\"Externe Marktteilnehmer\", EMP), and it allows EMPs to send incentives or commands for load adjustments to local control boxes such as EMSs. EMPs can be separated into passive EMPs that are receiving meter measurements or derived data only, and active EMPs that are also capable of sending commands to local control boxes [8].  
In the future, _Smart Meters_ will be compulsory for new installations, and according to the _German Metering Point Operation Law_ (\"Messstellenbetriebsgesetz\", MsbG) [6] the installation of SMGWs follows a step-wise roll-out plan, ultimately making it compulsory for consumers above 6000 kWh/year, or for consumers with renewable feed-in above 7 kW\({}_{\text{peak}}\). For consumers falling below these thresholds, the SMGW is optional and, hence, also the option to automatically communicate _Smart Meter_ measurements to entities outside the building or to receive load-shifting incentives from external parties. In the following, unless explicitly stated otherwise, we refer to _Smart Meters_ simply as _meters_.
## 3 Results and Discussion
### Weighted Comprehensive Index Evaluation Method
#### 3.7.4 Calculation of Composite Index  
The indexes of molecular characterization, physiochemical properties and structural characterization were calculated according to the weight of each index followed by the formula: \(GQI=\sum_{i=1}^{n}a\dot{i}\dot{r}i\) and the composite index of chitosan was obtained by adding the three indexes. The results are presented in Figure 9. From the overall results, the most significant feature is that the CSS composite index (1.31) is obviously higher than CSC (0.94) and CS's (0.85), which is consistent with the research results recently published [17,19]. In addition, molecular structure accounts for the largest proportion in the comprehensive index, and physiochemical and bio-functional also hold a large proportion. This is mainly due to the low viscosity average M\({}_{\text{W}}\) and high DD of CSS, and the parallel structure between \(\beta\)-chitosan molecules and weaker molecular binding force, which is superior to \(\alpha\)-chitosan (CSC and CS) in molecular structure, physical and chemical properties and biological functions.  
The higher the weighted composite index value, the better the chitosan biochemistry performance. The advantage of the weighted composite index method for evaluating chitosan biochemistry performance is that it can make the proportion of each index in the overall performance analysis clear, and can analyze and evaluate more objectively and comprehensively. The rationality of the comprehensive evaluation results mainly depends on the selection of representative indicators, the setting of reasonable weight coefficients  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Molecular Structural**} & \multicolumn{2}{c}{**Physiochemical**} & \multicolumn{2}{c}{**Bio-Functional**} \\ \cline{2-9}  & **M\({}_{\text{w}}\)** & **DD** & **WBC** & **FBC** & **TS** & **PS** & **SS** \\ \hline CS & 0.75 & 0.91 & 0.72 & 0.84 & 1.09 & 0.94 & 0.87 \\ CSC & 0.87 & 1.02 & 0.92 & 0.91 & 0.97 & 1.01 & 0.93 \\ CSS & 1.93 & 1.08 & 1.32 & 1.22 & 0.94 & 1.04 & 1.23 \\ \hline \hline \end{tabular}
\end{table}
Table 8: The index value after standard normalization.  
Figure 9: Composite index of chitosan biochemistry performance. Different lowercase letters under the data in the same group indicate significant difference (\(p<0.05\)).  
and the use of scientific statistical methods. In order to make the evaluation results of constant quality indicators comparable, it is necessary to determine a consistent evaluation index system and weight coefficient. Reasonable evaluation method can better control biological applications of chitosan obtained from different marine resources.
# Requirements and Hardware Limitations of High-Frame-Rate 3-D Ultrasound Imaging Systems
## 2 Materials and Methods
### Case Study: ULA-OP 256 towards 3-D High Frame Rate Imaging
#### 2.3.3 New Ring Topology  
To overcome the B\({}_{\rm{MC}}\) limitation, a new communication channel between the DSPs hosted by \"adjacent\" FE boards has been established by exploiting the SRIO link. The new topology transfers the baseband samples from the DSPs of one FE to the DSPs on a neighboring FE board. Specifically, as shown in Figure 2 (right panel), the DSPs of FE\({}_{1}\) transfer the baseband partially beamformed samples to the DSPs of FE\({}_{2}\). These DSPs coherently sum such samples with the partially beamformed samples created by FE\({}_{2}\), thus contributing to a 64-channel beamformed image. This operation is repeated until the last installed FE board, FE\({}_{\rm{N}}\), completes the beamforming addition. Therefore, in this topology, only the last board sends data to the MC board, and a transfer bandwidth B\({}_{\rm{MC}}\) = B\({}_{\rm{IQ}}\) rather than B\({}_{\rm{MC}}\) = N\({}_{\rm{FE}}\)\(\times\) B\({}_{\rm{IQ}}\) must be sustained. Furthermore, the transmission of final fully beamformed data, rather than of N\({}_{\rm{FE}}\) groups of partially beamformed data, correspondingly reduces the amount of data to be transferred to the MC board. The ring sum operations are performed in pipeline thanks to a special scheduling implementation, which exploits the multi-core architecture of the DSPs, thus no significative delays are introduced. Furthermore, it is worth highlighting that the acquisition task, which is the same in both topology, is handled in parallel to the elaboration chain, so, the ring topology does not affect in any way the TX/RX sequences of the scanner.  
Figure 2: Schematic diagrams of the star (**left panel**) and ring data transfer configurations (**right panel**). In the star topology, all the FE boards send the beamformed lines to the MC board at the same time, so that the B\({}_{\rm{MC}}\) requested to sustain the transfer is N\({}_{\rm{FE}}\) times higher than the output bandwidth of each FE (B\({}_{\rm{IQ}}\)). In the ring topology each FE board sends the data to the neighbor board. The last FE board sends the final beamformed line to the MC board. The B\({}_{\rm{MC}}\) requested to sustain the transfer is thus equal to the output bandwidth of a single FE board.
## 4 Discussion
### Practical Recommendations  
The findings of this study suggest that sand-based training may be advantageous for improving jumps relying on a long SSC and short sprints compared to training on hard surfaces. Both training surfaces seem effective at improving CoD performance, likely through distinct underlying adaptations. However, sand-based training has been shown to be more metabolically demanding. As such, it is suggested that when training on sand with the aim of enhancing speed performance, shorter exercise durations and longer recovery periods should be implemented to optimize performance and minimize muscle acidosis. Moreover, it is likely that sand-based training offers unique benefits by targeting specific underlying qualities crucial to athletic performance. Due to the inherent instability of sand, engaging in sand-based training leads to increased muscle activation and enhancements in motor unit recruitment, stability, and balance. Furthermore, the force-absorbing nature of sand requires individuals to generate greater muscular effort during the concentric phase of movement to overcome the resistance provided by the shifting sand. This increased demand on concentric muscle actions during sand-based training leads to specific adaptations that prioritize the development of concentric force production capabilities. As a result, there may be a simultaneous reduction in adaptations related to eccentric performance as the training focus shifts toward concentric muscle actions. The same force-absorbing properties of sand reduce any impact on the body's structures when performing high-intensity exercises, such as jumping or speed drills. Considering these factors, incorporating sand-based training in a team sport context is recommended from two perspectives. First, it can be implemented during the initial general preparation phase of a periodized training program in order to prepare the body's structures for the higher subsequent impacts while also targeting the specific qualities mentioned earlier. Sand-based training is particularly useful in this initial phase, as here, variation rather than high levels of specificity is usually favored. This approach helps to adapt the body gradually to subsequently increasing requirements and to reduce the risk of overloading. Second, the reduced impact and increased demands on balance and stabilization make sand-based training beneficial for reintroducing players to specific loads on hard surfaces after recovering from injuries, as it allows for a controlled and progressive transition, promoting rehabilitation and minimizing the risk of re-injury. However, sand-based training cannot replace training on hard surfaces, as training must meet the specific demands of the competition, and most team sports compete on hard surfaces. Therefore, emphasis should be placed on planning a training progression that favors a transition from sand-based to hard surface training, which applies to both post-injury rehabilitation and performance development training.
## 3 Results and Discussion
### Representing the ABFPs with a Reduced Subset
#### 3.3.1 The Selection of the Best Representative Subset
They are starPep_00002, starPep_04044, starPep_05305, starPep_09934, and starPep_10637, with all being reported as non-toxic except starPep_00002. It was not by chance that the privileged scaffold of starPep_04044 was also selected, which indicates that the scaffold extraction algorithm implemented in the StarPep toolbox works. Figure 6: Overlapping subsets 6, 7 and 8 resulting from the union of the harmonic and hub-bridge centralities at 0.35, 0.40 and 0.45 over the HSPN model. (**A**) 52 ABFPs from the HC \(\cup\) HB of the subset 8 (cutoff 0.35) over the total 174 ABFPs; (**B**) 66 ABFPs from the HC \(\cup\) HB of the subset 7 (cutoff 0.40) over the total 174 ABFPs; (**C**) 85 ABFPs from the HC \(\cup\) HB of the subset 6 (cutoff 0.45) over the total 174 ABFPs. Figure 7: HSPNs visualization of the representative subset integrated by 66 ABFPs from the HC \(\cup\) HB of the subset 7 (cutoff 0.40) using the Fruchterman-Reingold layout. Peptides communities are represented by different colors while the nodes size was scaled according to harmonic (**A**) and hub-bridge centrality (**B**) measures. The subset of 66 representative ABFPs extracted from the HSPN model is described in Supplementary File S4, which includes their IDs, sequences, lengths, the cluster to which they belonged in the HSPN model, and their centralities measures both in the HSPN model and in the newly constructed HSPN (Figure 7). This non-redundant subset of 66 peptides represents the chemical space of the ABFPs, and it is advisable use it as a reference to map new ABFP sequences as well as in multi-reference similarity searches against unlabeled peptide datasets and for design purposes. Other network-based analyses of the metadata associated with these 66 ABFPs will provide a different insight for the selection of privileged scaffolds for the development of next-generation antimicrobials.
# A Novel Complex-Valued Blind Source Separation and Its Applications in Integrated Reception
## 6 Simulation Analysis
### Target Range and Velocity Estimation  
The integrated system needs to accomplish the detection task, which requires the estimation of the target distance and velocity. The JADE algorithm in [17; 18; 20] is selected for comparison. Let SNR = 0 dB (array level) and JSR = 30 dB. If pulse compression is performed directly on the observed signal, as shown in Figure 8, multiple blurred false targets will appear and the distance of the target cannot be obtained.  
The observed signal is separated using the JADE and pulse compression is performed on the radar echo signal. As shown in Figure 9, multiple spikes appear and the distance at the first spike is not the true distance of the target, resulting in incorrect target detection. After performing pulse compression on multiple pulses, multiple blurred points appear and the distance of the target cannot be estimated. This is caused by the low separation accuracy of the JADE under a low SNR and high JSR.  
Figure 10 shows the pulse compression for single and multiple pulses after the separation of the observed signals by the proposed algorithm. Pulse compression of a single pulse shows that the first spike is approximately 700 higher in amplitude than the second spike, and the target distance can be successfully detected. After compressing multiple pulses, a clearer line can be seen, and thus the target distance can be estimated.  
Figure 8: Pulse compression of the observed signal. (**a**) Single pulse, (**b**) multiple pulses.  
Figure 7: DOA estimation RMSE and BER under different SNR. (**a**) DOA estimation RMSE under different SNR, (**b**) BER under different SNR.  
Figure 11 shows the three-dimensional and two-dimensional plots of the pulse Doppler processing of the radar echo signal, using the proposed algorithm. A peak and a bright spot can be seen in the three-dimensional and the two-dimensional plot, respectively. The proposed algorithm can successfully estimate the target distance and velocity.
# Subpath Queries on Compressed Graphs: A Survey
## 3 Indexing Labeled Graphs and Regular Languages
### Indexing Labeled Trees
#### 3.5.5 Compression  
To conclude, we show how the XBWT (and, similarly, the BWT of a string) can be compressed. In order to efficiently support the LF mapping, sequence \(IN\) has to be explicitly stored. However, note that this sequence is strictly increasing. If the alphabet is effective and of the form \(\Sigma=[1,\sigma]\), then we can represent \(IN\) with a simple bitvector of \(n\) bits marking with a bit '1' the first occurrence of each new character. If \(\sigma\) is small (for example, \(\text{polylog}(n)\)), then this bitvector can be compressed down to \(o(n)\) bits while supporting efficient queries [71]. Finally, we concatenate all characters of \(OUT\) in a single string and use another bitvector of \(2n\) bits to mark the borders between each \(OUT[i]\) in the sequence. Still assuming a small alphabet and using up-to-date data structures [69], this representation takes \(nH_{0}+2n+o(n)\) bits of space and supports optimal-time count queries. In their original article, [PERSON] et al. [35] realized that this space could be further improved: since nodes are sorted in co-lexicographic order, they are also clustered by the paths connecting them to the root. Then, for a sufficiently small \(k\in O(\log_{\infty}n)\), we can partition \(OUT\) by all distinct paths of length \(k\) that reach the nodes, and use a different zero-order compressor for each class of the partition. This solution achieves high-order compressed space \(nH_{k}+2n+o(n)\).  
Another method to compress the XBWT is to exploit repetitions of isomorphic subtrees. [PERSON] et al. [48] show how this can be achieved by a technique they call _tunneling_ and that consists in collapsing isomorphic subtrees that are adjacent in co-lexicographic order. Tunneling works for a more general class of graphs (Wheeler graphs), so we discuss it more in detail in Section 3.7. Similarly, one can observe that a repeated topology will generate long runs of equal sets in \(OUT\)[49]. Let \(r\) be the number of such runs. Repetitive trees (including repetitive texts) satisfy \(r\ll n\). In the tree in Figure 7, we have \(n=9\) and \(r=7\). For an example of a more repetitive tree, see Reference [49].
# Comparison of Methods to Identify and Monitor Mold Damages in Buildings
## 1 Introduction
Nevertheless, there is still little scientific evidence demonstrating the validity of qPCR approaches to detect hidden mold damages from dust samples, as it may be difficult to distinguish the moisture indicator fungi from the baseline fungi [13]. Only a few HTS studies have coupled the analysis of mold-damaged materials and dust in the same buildings [24; 25; 26]; thus, this combined approach should be further explored in order to detect indicator taxa, as stated by [PERSON] et al. [27]. Considering the mentioned knowledge gaps, the overarching aim of this study was to evaluate the use of two methodological approaches, the direct microscopy of visible mold growth and the DNA metabarcoding of both mold and dust samples, for identifying and monitoring the fungi associated with mold-damaged residential buildings in Oslo, Norway. Furthermore, by analyzing dust samples from different rooms, this study assessed the potential of DNA metabarcoding for detecting the spreading of indicator fungi from the colonized materials (possible sources) to other building areas.
## 3 Hardware and Software Setup
### Memory Access Management  
Our parallelization of FastSLAM 2.0 is designed to run on a heterogeneous architecture, where the CPU and the GPU, which can be defined as _host_ and _device_, respectively, are equipped with two separate memories and execute commands asynchronously. The parallelized FastSLAM 2.0 is interfaced with a simulator, described in Section 4.1, that runs on the CPU and feeds the algorithm with synthetic sensor data. The CPU can also allocate memory on the GPU to store variables and launch kernels. All the FastSLAM 2.0 pipeline operations leverage either the GPU global memory or the on-chip memory (shared memory and local thread memory). The final results are eventually copied to the host device (CPU). A schema of the memory management is provided in Figure 3.  
The proposed implementation of FastSLAM 2.0 optimizes memory access to accelerate processing time. This is achieved by limiting access to the global device memory whenever possible in favor of shared memory when cooperative data manipulation is needed, as well as local thread memory.  
Shared memory in a GPU is fast, on-chip memory accessible by all threads within the same thread block. It enables efficient data sharing among threads and significantly improves the performance of parallel algorithms by allowing data reuse and reducing redundant calculations.  
While shared memory is much faster than global memory, offering latency and bandwidth comparable to registers, it is limited in size and can vary depending on each GPU architecture. The GPU architecture where we validate our approach is equipped with 48 kilobytes of shared memory per block. All threads within the same block can read from and write on shared memory. Shared memory is divided into banks. The bank size and the number of banks depend on each specific GPU architecture. If multiple threads belonging to the same warp access the same bank simultaneously, bank conflicts may arise, degrading performance. The memory bank conflict is commonly avoided through common CUDA programming good practices such as padding and accurate offset selection to ensure that all the threads in the same block contemporarily access different banks. Furthermore, conflicts can arise when thread divergence occurs. Therefore, it is utterly important to correctly execute thread synchronization to ensure that the read and write operations on the shared memory are coherently performed.  
Figure 3: Hardware–software architecture schema.
###### Abstract  
Accurate and efficient path loss prediction in mmWave communication plays an important role in large-scale deployment of the mmWave-based 5G mobile communication systems. Existing methods often present limitations in accuracy and efficiency and fail to fulfill the requirements of cell planning, especially in dense urban environments. In this paper, we propose a novel training method called multi-way local attentive learning, which allows for learning from multiple perspectives on the same set of training samples with local attention paid to each subset of the entire dataset. The sample data set can be partitioned in various ways with respect to different attributes, such that a larger amount of knowledge can be extracted from the same data set. The proposed scheme outperforms the existing schemes in terms of prediction accuracy at the average RMSE of 6.01 dBm.  
millimeter wave; path loss modeling; 5G; convolutional neural network; urban scenario +
Footnote †: journal: _electronics_  
00 April 2022  
Accepted: 5 June 2022  
Published: 7 June 2022  
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