esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Test Verification
### Measurement Result  
A commercial 12-bit 800 Msps 2-channel TI-ADC was used to validate the proposed calibration algorithm under real conditions. The measurement setup is illustrated in Figure 15, and it consists of a signal generator (SDG6032X), a commercial 2-channel TI-ADCs (ADC12 DL3200 EVM), and a field-programmable gate array (TSWDL3200) for data transmission.  
The features of experimental equipment can be shown in Table 5. Figure 16 shows the frequency spectrum before and after calibration. Additionally, the input is a single sinusoidal signal. The frequency of the input signal is 70 MHz. As shown in Figure 16, the SNDR and SFDR are, respectively, 17.15 dB and 17.17 dB. Additionally, the SNDR and SFDR are respectively 43.19 dB and 65.19 dB after calibration by the proposed algorithm. Additionally, it can also be seen from Figure 16 that the spurs of time skew drop below the noise floor. Therefore, the measurement result demonstrates that the proposed method is effective against time skew.  
Figure 14: The cumulative errors under different ENOB of the sub-ADC in the proposed technique.  
Figure 13: The cumulative errors under different SNR of the sub-ADC in the proposed technique.  
Figure 16: The frequency spectrum of the commercial 2-channel TI-ADCs before and after calibration.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Test Conditions** & **Typical Values** & **Unit** \\ \hline \multirow{2}{*}{The effective number of bits (ENOB)} & The input frequency is 2482 MHz & \multirow{2}{*}{8.0} & \multirow{2}{*}{Bits} \\  & (dual-channel mode) & & \\ \hline Resolution & Resolution with no missing codes & 12 & Bits \\ \hline \multirow{4}{*}{Differential nonlinearity (DNL)} & Maximum positive excursion from the ideal step size & \multirow{2}{*}{0.3} & \multirow{2}{*}{LSB} \\  & Maximum negative excursion from the ideal step size & \multirow{2}{*}{\(-0.3\)} & \\ \cline{2-3}  & Maximum positive excursion from the ideal transfer function & & \\ \cline{2-3}  & Maximum negative excursion from the ideal transfer function & & \\ \cline{2-3}  & Maximum negative excursion from the ideal transfer function & \multirow{2}{*}{\(-2.0\)} & \\ \hline Analog differential input full-scale range & Default & 800 & mV \\ \hline \hline \end{tabular}
\end{table}
Table 5: The features of experimental equipment.  
Figure 15: Experiment setup which is aimed to validate the proposed method.  
In Table 6, the calibration effects of [13; 16; 27; 30; 31], and this paper are shown. After calibration, there is the highest ENOB in the proposed technique compared with [13; 16; 27; 30; 31]. Figure 16 shows the frequency spectrum of the commercial 2-channel TI-ADCs before and after calibration for the two-tone sinusoidal signals. The frequencies of the input signal are 70 MHz and 170 MHz. As shown in Figure 17, the SNDR and SFDR are respectively 11.83 dB and 10.03 dB. Additionally, the SNDR and SFDR are, respectively, 42.38 dB and 56.35 dB after calibration by the proposed algorithm. Therefore, the proposed algorithm also has a good calibration effect for actual two-tone signals.
# Stratosphere: A Fast Virtual Circuit Switch Layer for Data Center Network
## 2 Related Work
### Packet Switching  
Both packet switching and message switching belong to store-and-forward switching. Data is stored at the path node and then forwarded, which improves the utilization rate of the communication link. Among them, the message switching [8] regards the entire message as the unit of storage and forwarding, which may cause a large delay. Packet switching divides the information to be transmitted into several packets as the unit, and the delay is shorter than that of packet switching. Figure 2 shows the process of packet switching. It can be used in Ethernet [9; 10] and optical networks [11; 12; 13]. According to different transmission protocols and transmission paths, packet switching can be divided into datagram packet switching and virtual circuit packet switching.  
Datagram packet switching [14] uses each datagram as a data transmission unit and performs processing procedures such as path selection independently. A variety of routing protocols and forwarding technologies are used to support datagram packet switching and improve datagram forwarding speed, such as EGP, IGP, and so on.  
Virtual circuit packet switching [15] combines circuit switching with packet switching to build connections between sender and receiver before communicating. Each node in the connection has a data buffer, which performs output queuing and port forwarding according to the direction of the connection and the received packets.  
The X.25 protocol [16] is used for data services. It provides end-to-end confirmation and has certain reliability, but the confirmation and error control of each node will cause unnecessary delays. Frame Relay is an optimization technology of X.25. It uses the frame as a unit to transmit data, which shortens the transmission delay and has a low overhead. ATM (Asynchronous Transfer Mode) [17; 18] technique uses cells as the unit of packet switching and adopts asynchronous time division multiplexing for transmission. ATM has scalability and low latency.
# Collective Communication Performance Evaluation for Distributed Deep Learning Training
## 2 Background  
Data Parallel Distributed Deep Learning Training Architectures: Parameter Server vs. Ring All-Reduce  
Distributed data parallel (DDP) is a technique designed for the scalable and efficient processing of deep learning training. Essentially, DDP needs the distribution of gradients and parameters associated with deep learning across multiple GPU nodes, allowing for concurrent training across these nodes. Figure 1 illustrates the parameter server architecture. It consists of a central parameter server, with each worker node maintaining communication with this server to synchronize model parameters. Each worker operates independently, handling a portion of the data and subsequently transmitting the outcomes to the parameter server. To facilitate efficient communication among workers, two methods are employed: broadcast [19] and gather (allgather) [20]. The broadcast method ensures uniform model updates by transmitting parameters from the parameter server to all worker nodes. Conversely, the gather (allgather) method collects the results computed by individual workers and subsequently conveys these results to the parameter server for consolidation and updating.  
Figure 2 depicts the ring all-reduce architecture. In this setup, multiple worker nodes are organized in a circular ring configuration. Each node within the ring possesses data of identical size, transmitting model parameters that necessitate updates while also receiving data from other nodes. At each node, the received data are merged with its own data, resulting in the generation of new data, which is then passed on to the next node in the ring. This process continues iteratively, with data propagating along the ring until all nodes eventually acquire the same updated data.  
Figure 1: Parameter server architecture.  
Figure 2: Ring all-reduce architecture.
# A Review of Modelling and Simulation Methods for Flashover Prediction in Confined Space Fires
###### Abstract  
Confined space fires are common emergencies in our society. Enclosure size, ventilation, or type and quantity of fuel involved are factors that determine the fire evolution in these situations. In some cases, favourable conditions may give rise to a flashover phenomenon. However, the difficulty of handling this complicated emergency through fire services can have fatal consequences for their staff. Therefore, there is a huge demand for new methods and technologies to tackle this life-threatening emergency. Modelling and simulation techniques have been adopted to conduct research due to the complexity of obtaining a real cases database related to this phenomenon. In this paper, a review of the literature related to the modelling and simulation of enclosure fires with respect to the flashover phenomenon is carried out. Furthermore, the related literature for comparing images from thermal cameras with computed images is reviewed. Finally, the suitability of artificial intelligence (AI) techniques for flashover prediction in enclosed spaces is also surveyed.  
flashover; artificial intelligence; CFD software; prediction; thermal vision camera; thermal image +
Footnote †: journal: Applied Sciences
# Review of Channel Estimation for Candidate Waveforms of Next Generation Networks
## 6 Findings and Possible Research Directions
Thus, the Non-PR condition is often the case in reality. This gives rise to the need for more sophisticated CE algorithms suitable for Non-PR condition. It is also observed from this study that CE is not well studied for NC-OFDM system. NC-OFDM systems offer higher data rates; as such, they are very useful in cognitive radio applications which are well designed to effectively manage and efficiently utilise the scarce available frequency spectrum for an eMBB communication system. Therefore, extensive CE research for NC-OFDM systems can further improve the quality of transmitted signal's reception as well as total system throughput over fading channels for enhancement in user QoE. It is worthy to mention that machine learning technologies can also be exploited as a possible research topic, where unsupervised software-based CE and prediction algorithms can be developed for estimating the frequency and time dispersion characteristics of the multipath fading channel in application to the above-mentioned waveform candidates of the 5G networks and beyond. For reference, refer to [153] Finally, massive MIMO systems (which adopt large-scale transmit antenna arrays) and other radio access techniques (such as RSMA, NOMA and SCMA) are pertinent technologies that form technical components of the upcoming 5G networks and beyond. Joint channel estimation, carrier frequency offset estimation and interference cancellation/decoding schemes are also useful techniques that can considerably enhance the throughput and spectral efficiency of these wireless communication systems. Consequently, developing novel algorithms that will jointly estimate the channel characteristics, the carrier frequency offset, as well as interference cancellation methods for performance enhancement of the mentioned xG technologies is an open research direction for the emerging 5G networks and beyond.
# Toward a Detailed Evaluation of Wireless Industrial Data Distribution Approaches +
## 3 Methods for Industrial Data Distribution
### Centralized Methods for Industrial Data Distribution  
The composition of the two centralized methods is shown in Table 2 labeled as C1 and C2. Method C1 is operating as follows: publish-subscribe on the application level, RPL in non storing mode on the network level, CSMA/CA on the MAC level, and IEEE 802.15.4 on the physical level. Method C2 on the other hand is operating as follows: publish-subscribe on the application level, source routing on the network level, WirelessHART on the MAC level and IEEE 802.15.4 on the physical level.  
The common aspect of the centralized methods is that they cache the generated data at the CNC. Consequently, when a consumer node requires data access, the data request has to be sent to the CNC and the CNC has to make the data available to the consumer. The main differences between the two centralized methods are in the MAC and NET layers. Method C1 uses RPL in non-storing mode in order to route the relevant data over the typical CSMA/CA protocol of 802.15.4. That means that the slots in each frame are generally contention-based. By contrast, method C2 uses source routing over the WirelessHART MAC protocol. In this case, the use of TDMA and pre-scheduled timeslots prevents message collisions, and frequency hopping and retransmissions limit the effects of temporal and frequency interference (a retransmission occurs on a different frequency).
## 4 Discussion
#### Limitations of the Study  
The limits regarding this review should be considered. Most of the articles selected were case reports and there is currently no way to validate the quality standard of this type of study. For this reason, we have adapted the ROBINS-I tool [7] to evaluate the quality of the selected case reports. In addition, the inclusion criteria we used in our search strategy enabled us to analyze 45 articles. In vitro, non-English and pre-1990 studies have not been selected. Nonetheless, the exclusion of these papers should be considered as a limitation of this review.
## 3 Results and Analysis
### Determination of Optimal MLSP During Intersseason  
Considering the variability of OA conditions throughout the year in South Korea, this study determined the optimal MLSP for the interseason, excluding October, which served as the primary evaluation period. Figure 9 shows the cooling energy consumption for each month during the interseason. In April and May, Cases #6 and #5 exhibited the lowest cooling energy consumption, respectively. The lowest cooling energy consumption extended from Cases #2 to #7 in March and from Cases #3 to #7 in November. This variation in optimal MLSP each month was due to monthly changes in OA conditions. During this period, the cooling energy consumption was consistent across Cases #2 to #7 in March and across Cases #3 to #7 in November, as the OA temperatures in March and November did not exceed 19 and 20 \({}^{\circ}\)C, respectively, resulting in uniform OA intakes. This analysis highlights the importance of defining MLSP according to the specific OA conditions of each month to maximize cooling energy savings through economizer control.  
Figure 8: Indoor thermal environment by varying MLSP: (**a**) indoor air dry-bulb temperature; (**b**) indoor air relative humidity.
## 4 Apertureless-Type SNOM
### Infrared Nano-Imaging  
Both infrared imaging and FTIR have been coupled to a SNOM system, and have been used successfully using s-SNOM [42; 43; 44; 45]. In this arrangement, the sample is illuminated with far-field IR light with the resulting near-field scattered from an s-SNOM tip. In order to collect FTIR spectra, the AFM-SNOM tip arrangement need not be modified; only additional detectors and optics are required. FTIR SNOM mapping has been used to great effect on other systems including biological systems, semiconductor devices or thin film stacks may be investigated with nano-FTIR systems [38; 42; 43; 44]. The high resolution of SNOM allows the structure of single proteins to be imaged [43], along with collection of a nano-FTIR of neighboring proteins.  
Access to sub-wavelength features of thin films can provide otherwise inaccessible information including polymorphism or phase coexistence in organic films. Figure 4a,b depict AFM and IR s-SNOM image of a pentacene thin film recorded using mid-infrared illumination, respectively [45]. The topography image alone is not sufficient to identify two different phases of pentacene, thin film or bulk phase, where the bulk phase is associated with larger grains and poor charge carrier mobility [45]. The corresponding IR s-SNOM image however, clearly identifies a mixed phase with larger ellipsoidal grains throughout the films. This is confirmed using nano-FTIR (Figure 4c) with peak fitting which clearly identifies a shift in the resonance frequency of the bulk phase (green line, yellow ellipsoids in the image) compared to the surrounding thin film phase (blue line, red areas in the images).  
Figure 3: S-SNOM images of graphene plasmons. (**a**) 1 mm wide graphene nano ribbons on an SiC substrate. The black and white images show AFM morphology, while the colored images were taken with 9200 nm (left), 9681 nm (middle) and 10,152 nm (right) light wavelengths. The line plots on the panels at the far left and right edges represent the near-field images for \(\lambda_{\mathrm{o}}\) = 9200 nm and \(\lambda_{\mathrm{o}}\) = 10,152 nm; (**b**) Tapered graphene ribbons on the same SiC substrate imaged with SNOM, where the arrows indicate localized resonant modes. Reproduced with permission from [38], Copyright Nature, 2012.
# Mining Deformation Life Cycle in the Light of InSAR and Deformation Models
## 1 Introduction
### InSAR Deformations
When the exploitation layer is shallower (300-450 m from the surface), a subsidence was observed within three to four months after excavation, while the surface deformation can be observed up to two to three years after the end of exploitation in the case of thick carboniferous rocks covering the coal seam. The author also showed that the direction and amount of subsidence depends on the dip of the coal seam, resulting in the deviation of mining influences described in [16; 26]. The first study of the subsidence in the range of the Bytom mine area in USCB in which SAR observations are used was done in [26]. The study considered long-term subsidence related to coal and Pb mining. The Permanent Scatterer methodology was applied. Another study by the same author [17] showed subsidence in Katowice due to mining. For a similar subsidence in InSAR and levelling, a 5-6 mm difference was shown. The author argued that complex geological structures can affect the appearance of the subsidence, shifting it in time and space, such as in the case of Bytom. The dip of the extraction (following the seam) can have the effect of shifting 7.7 cm over 35 days. In an interesting study [20], a subsidence over the Bobrek mine in the Miechowice district of Bytom was investigated using two different techniques, PSInSAR and DInSAR. There were also studies [27] that showed the tools to combine deformation with geographic information systems, geology, and FEM modelling, allowing a more comprehensive analysis of mining-induced processes, not only subsidence.
# Mutual-Information-Based Incremental Relay Communications for Wireless Biomedical Implant Systems
## 4 The Proposed Protocol  
In this section, a novel communication protocol for wireless biomedical implanted networks is demonstrated. Considering the limited battery of the implanted sensor and that every piece of abnormal medical data information is potentially life-critical, we aim to prolong the network lifetime and maintain the communication link quality. A communication link can be established only when the mutual information value \(I\left(D_{ij}D_{j}\right)\) value becomes smaller than the predetermined threshold. The communication direction can be either from \(S\) to \(D\) or from \(S\) to \(R\); then \(R\) forwards the data collected to \(D\), depending on the transmission distance.
# Imaginary Speech Recognition Using a Convolutional Network with Long-Short Memory
## 5 Discussion
### CNNLSTM vs. CNN  
The main advantage of a LSTM neural network is its capability of memorizing long-term dependencies. This neural network ability comes in handy when non-stationary time variant signals, such as EEG, are analyzed. In addition to the LSTM long-term memory, the spatial dependencies of the EEG signals were taken into consideration by adding CNN to the LSTM layer. This junction helped the neural network to learn both spatial and time-variant features, increasing the accuracy of the system from 0.37, obtained with a CNN architecture with similar parameters up to 0.43.  
The mean confusion matrix of the k-folds presented in Figure 5 reveals that there is hardly any confusion between the phonemes and words, as expected after the visualization of the LDA dimension reduction results in Figure 2. Confusions were made between the phonemes /tiy/ and /diy/, with a relatively higher percent, compared to the rest of the phonemes. This behavior can be explained by the similar mechanisms of the pronunciation of the sound \"t\" and the sound \"d\". For both utterances, the vocal tract maintains almost the same position, and the sound is being produced by presence or absence of vocal cord vibration. Major confusions of approx. 19% were also made between the words \"pat\" and \"pot\", for the same reason of similar mechanism of pronunciation.  
In Table 4, we computed the Euclidean distance between the mean of each phoneme and word analyzed for the cross-covariance in the frequency domain. We observed that these distances are smaller than the ones computed for the extracted features obtained after the first CNNLSTM layer (Table 5), meaning that, after the signal was processed by the firstneural network layer, the feature space of the utterances was modified, and the features became more separable.
## 5 Conclusions  
Pseudomonas sp. No. 3, Enterobacter sp. No. 26, Enterobacter sp. No. 34, Pseudomonas sp. No. 35, Ralstonia sp. No. 50, Bacillus sp. No. 62, Arthrobacter sp. No. 146, Breviobacillus sp. No. 2-18, and Paenisporosarcina sp. No. 2-60 were nine strains of bacteria found for the first time to inhibit R. solani growth via VOCs. The inhibitory effect ranged from 7.84% to 100%, with Ralstonia sp. No. 50 inhibiting the growth of R. solani completely (100%). Benzoic acid ethyl ester, 3-methyl-butanoic acid, 2-ethyl-1-hexanol, 3-methyl-1-butanol, and 6-methyl-5-hepten-2-one were VOCs released by bacteria in the rice rhizosphere. These five VOCs were found to be toxic to R. solani for the first time. Among them, benzoic acid ethyl ester, 3-methyl-butanoic acid, and 2-ethyl-1-hexanol were lethal to R. solani. Overall, novel functions of bacterial strains from the rice rhizosphere and their VOCs have the potential to be used as resources for biological control of rice sheath blight in the future.  
Conceptualization, F.F., A.S. and E.W.; methodology, E.W., J.B., S.W. and J.Z.; formal analysis, E.W., Z.S., X.L. ([PERSON]) and W.D.; writing--original draft preparation, E.W.; writing--review and editing, F.F., A.S., M.C., and X.L. ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
The research was funded by the National Key Research and Development Program of China (2019 YED1002004) and Fundamental Research Funds for Central Non-profit Scientific Institution (No. 1610132019011, 1610132020012).