esa-sceva/satcom-chunk-collection huggingface dataset:
# Protein Dynamics in F-like Bacterial Conjugation
## 4 Mating Pair Stabilization Proteins and the Dynamics of their Structural Ensemble
### TraG and the Structural Dynamics Effectuating its Trifunctional Roles
All of these structures feature a nuclear transport factor 2-like fold and show high structural homology despite low sequence homology, with backbone root mean square deviation (rmsd) values ranging between 2.6-2.8 A in comparing all structures [119]. These VirB8 structures indicate the protein functions as a dimer; however, TraH is monomeric. A VirB8 homolog from pKM101, TraE was shown to form hexamers when isolated, and when interacting with the VirB6 homolog from this plasmid, TraD, thus, implying the diversity in oligomerization possible in these systems [120]. There are no high-resolution structures of VirB6, however structural predictions have shown there are 4-9 TM helices in VirB6 from the Ti plasmid, while the NTD of TraG is predicted to span the membrane 3-5 times [79]. These studies indicate the potential for TraG to have a C-terminus similar in structure to VirB8, and therefore may function as a monomer, a dimer, or a hexamer. As the TraG* orthologue is commonly embedded into the IM from its N-terminus in all of the described structures, it is probable that TraG* from F-like T4 SS will not oligomerize into its proper complex without the membrane-bound half. Recent studies on TraG* have shown that the region bridging the N-terminal IM bound domain and TraG* serve as a flexible linker region ([PERSON] and [PERSON], in preparation). This was first theorized when the aa sequence for TraG was entered into the phase separation predictor (PSP) software for predicting phase separating IDRs with long range pi-pi contacts [34]. The region from residues Pro447-Gln498 was predicted to cause phase separation in the protein, with 27 of these residues surpassing the score threshold of 4.0 thus providing TraG with an overall score of 4.06. Further characterization of TraG* truncation mutants showed that this predicted phase separating region was merely highly dynamic and likely serves to allow for various conformations to be achieved by the CTD of TraG in performing its Mps and Exx functions despite being anchored to the IM ([PERSON] and [PERSON], in preparation). Due to the multifunctional properties of TraG in the F-like T4 SS, targeting this protein for rational drug design may provide new avenues for the development of antibiotics against bacterial conjugation; a high-resolution structure of the protein would greatly aid this process.
# Sparsity-Inducing Super-Resolution Passive Radar Imaging with Illuminators of Opportunity
## 4 Simulation and Analysis
### Imaging Performance versus Measurements  
In this experiment, we compare the reconstruction performance of different super-resolution algorithms in different amount of available measurement data. The used measurement data are 50%, 25% and 12.5% of the passive radar echo data, respectively, which can be obtained through two, four and eight times uniform decimation. Suppose the input SNR is 10 dB. The comparative imaging results are shown in Figure 8. The first, second and third rows represent super-resolution passive radar image obtained by the ESPRIT, RELAX and proposed CS algorithms, respectively. It is noticeable that the proposed CS algorithm can achieve more accurate reconstruction than both the ESPRIT and RELAX algorithms, even if the measured data is only 12.5% of the passive radar echo data.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{3}{c}{**Super-Resolution Algorithm**} \\ \hline
**SNR (dB)** & **ESPRIT** & **RELAX** & **Proposed CS** \\ \hline
20 & 0.3560 & 0.0095 & 0.0023 \\
10 & 0.4045 & 0.0154 & 0.0025 \\
5 & 0.4608 & 0.0279 & 0.0029 \\ \hline \hline \end{tabular}
\end{table}
Table 3: MSEs of the estimated position using the ESPRIT, RELAX and proposed CS algorithms under different signal-to-noise ratios (SNRs).  
Figure 8: Passive ISAR imaging results with different reconstruction methods under different measurements.  
It can be noticed from the estimation MSEs given in Table 4 that the ESPRIT and RELAX algorithms are heavily influenced by the amount of measurement data. In contrast, the proposed CS algorithm achieves satisfactory MSE even with very limited amount of measurement data. This validates again that the proposed CS algorithm is very effective for super-resolution radar imaging in limited measurement cases. However, when the used measured data is less than \(1/16\) of the original passive radar echo data, the imaging performance of the proposed framework will be seriously deteriorated, and corresponding MSE of the proposed CS algorithm is about 0.1625.
# Bridging the Appearance Domain Gap in Elderly Posture Recognition with YOLOv9
## 4 Results  
### Training Results  
In this paper, we compared the performance of the YOLOv9 model trained on two different datasets: a pure virtual dataset and a hybrid dataset. The results of the performance during the training phases are summarised in Figure 3.  
Figure 3 shows that the trained YOLOv9 model was able to accurately detect and classify the postures of older people in both a virtual and a hybrid home environment. The model consistently identified the three postures--_standing_, _sitting_, and _lying_ --with high confidence. The model showed consistent performance with similarly high precision, recall, mAP50, and mAP50-95 values for both environments.  
Notably, the confidence values were mostly between 0.92 and 0.95, with a maximum of 0.97 and a minimum of 0.85.
## 2 Geological Background  
In recent years, a high concentration of helium was detected in the Dongsheng gas field at the northern margin of Ordos Basin. [PERSON] collected 92 natural gas samples for helium concentration analysis. The results showed that the average helium concentration in the Dongsheng gas field is 0.133%, with over 70% of the helium concentration exceeding 0.1% [15]. Therefore, the Dongsheng gas field was selected for helium detection during drilling. The Dongsheng gas field is located in the northern part of the Ordos Basin, which spans three tectonic units: the Yishan slope, the Tianhuan depression, and the Yimeng uplift (Figure 1a) [21]. The test wells were located between the Boerjianghaizi fault and the Wulanjilinnmiao fault (Figure 1b). Figure 1c shows a comprehensive stratigraphic histogram of the Permian and underlying strata in the area. The reservoir in this area is the Lower Shihezi Formation of the Lower Permian (P\({}_{1}\)x), and the source rocks are coal, carbonaceous mudstone, and dark mudstone of the Upper Carboniferous Taiyuan Formation and the Lower Permian Shanxi Formation [22; 23]. The source rocks of the Taiyuan Formation and Shanxi Formation are basically shallow in the north and deep in the south, according to their geological history. Their vitrinite reflectance also increases gradually from northeast to southwest, with the Ro value exceeding 1.4%, and the rocks entering a high-maturity stage, typical of good gas source rocks [24]. Due to the influence of tectonic events, the source rocks were rapidly buried from the Late Triassic to the end of the Early Cretaceous, generating a large amount of gas. This was a critical period for the formation of gas reservoirs [25]. The lithology of the reservoir shows that is mainly composed of coarse-to-medium-grained lithic sandstone with gravel in the P\({}_{1}\)x section, and the cap rocks is composed of mudstone and sandy mudstone of the Upper Shihezi Formation and Shiqianfeng Formation [15; 26; 27].  
Figure 1: Location of the Dongsheng Gas Field in the Ordos Basin (**a**), location of the test wells (**b**), and comprehensive stratigraphic histogram (**c**). (Modified after [15]).
###### Abstract  
Earth Observation (EO) spectral indices have been an important tool for quantifying and monitoring forest biomass. Nevertheless, the selection of the bands and their combination is often realized based on preceding studies or generic assumptions. The current study investigates the relationship between satellite spectral information and the Above Ground Biomass (AGB) of a major private forest on the island of Java, Indonesia. Biomass-related traits from a total of 1517 trees were sampled in situ and their AGB were estimated from species-specific allometric models. In parallel, the exhaustive band combinations of the Ratio Spectral Index (RSI) were derived from near-concurrently acquired Sentinel-1 and Sentinel-2 images. By applying scenarios based on the entire dataset, the prevalence and monodominance of acacia, mahogany, and teak tree species were investigated. The best-performing index for the entire dataset yielded R\({}^{2}\) = 0.70 (R\({}^{2}\) = 0.78 when considering only monodominant plots). An application of eight traditional vegetation indices provided, at best, R\({}^{2}\) = 0.65 for EVI, which is considerably lower compared to the RSI best combination. We suggest that an investigation of the complete band combinations as a proxy of retrieving biophysical parameters may provide more accurate results than the blind application of popular spectral indices and that this would take advantage of the amplified information obtained from modern satellite systems.  
remote sensing; data fusion; tropical forest; complete band combinations; vegetation indices +
Footnote †: journal: Geo-Informatics  
1
###### Abstract  
Remote sensing offers a non-destructive method to detect plant physiological response to the environment by measuring chlorophyll fluorescence (CF). Most methods to estimate CF require relatively complex retrieval, spectral fitting, or modelling methods. An investigation was undertaken to evaluate measurements of CF using a relatively straightforward technique to detect and monitor plant stress with a spectroradiometer and blue-red light emitting diode (LED). CF spectral response of tomato plants treated with a photosystem inhibitor were assessed and compared to traditional reflectance-based indices: normalized difference vegetation index (NDVI) and photochemical reflectance index (PRI). The blue-red LEDs provided input irradiance and a \"window\" in the CF emission range of plants (\(\sim\)650 to 850 nm) sufficient to capture distinctive \"two-peak\" spectra and to distinguish plant health from day to day of the experiment, while within day differences were noisy. CF-based metrics calculated from CF spectra clearly captured signs of vegetation stress earlier than reflectance-based indices and by visual inspection. This CF monitoring technique is a flexible and scalable option for collecting plant function data, especially for indicating early signs of stress. The technique can be applied to a single plant or larger canopies using LED in dark conditions by an individual, or a manned or unmanned vehicle for agricultural or military purposes.  
spectroscopy; chlorophyll fluorescence; vegetation indices; NDVI; PRI; photosynthesis; photosystem inhibition 20222223411. [[https://doi.org/10.3390/s22093411](https://doi.org/10.3390/s22093411)]([https://doi.org/10.3390/s22093411](https://doi.org/10.3390/s22093411))  
1
# Transformer-Based Cascading Reconstruction Network for Video Snapshot Compressive Imaging
## 2 Related Works
### Vision Transformer  
Transformer [22] is a classic model in natural language processing. The correlation between different texts is calculated through global matching, which improves the network's ability to learn the long range of association features. According to its excellent performance, Transformer has also been widely used in computer vision tasks such as image segmentation, image classification, object detection, etc.  
[PERSON] et al. proposed Vision Transformer (ViT) [25] for the first time, which decomposed the image into block sequences and input them as Tokens into the attention module for correlation calculation, which can extract the long-range features of the image and achieve excellent performance in classification tasks. [PERSON] et al. proposed Swin Transformer [26], which limited multi-head self-attention computation in non-overlapping local windows to improve computational efficiency. This method achieved cross-window information interaction through window shifting, which made it compatible with different visual tasks. [PERSON] et al. followed the hierarchical structure of the original Swin Transformer, expanding the scope of local attention calculation from the spatio domain to the spatiotemporal poral domain [27]. [PERSON] et al. proposed SwinIR [28] on the basis of Swin Transformer. This model had improved performance in image reconstruction, super-resolution and other tasks by using sets of residual Swin Transformer modules. [PERSON] et al. proposed the Meta-TR model in [29]. The model used Swin Transformer as a backbone and designed a meta-attention module, which converted the sensing matrix into an attention vector to guide the training of the compressive sensing reconstruction network and achieved a good balance in reconstruction performance, model parameter quantity and running time. [PERSON] et al. [30] replaced the full connection layer in the multi-head attention with the convolution layer to make better use of remote spatiotemporal correlations.  
Transformer has shown excellent performance in reconstruction tasks and computer vision problems. Therefore, we introduce Transformer into video snapshot compressive imaging tasks and propose a Cascading Reconstruction Network to achieve high-quality reconstruction.
## Abbreviations  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l}
3C & Communication, caching and computing \\ ACL & Access control list \\ AI & Artificial intelligence \\ API & Application programming interface \\ CFN & Compute first networking \\ CR & Cognitive radio \\ CS & Content store \\ CPU & Central processing unit \\ DDoS & Distributed denial of service \\ DT & Digital twin \\ DTN & Digital twin network \\ FIB & Forwarding information base \\ FPGA & Field-programmable gate array \\ GPU & Graphics processing unit \\ ICN & Information-centric networking \\ ICedge & Information-centric edge \\ IETF & Internet engineering task force \\ INC & In-network computing \\ IRS & Intelligent reflecting surfaces \\ IRTF & Internet research task force \\ IoT & Internet of Things \\ IP & Internet protocol \\ MEC & Multi-access edge computing \\ NACK & Negative acknowledgment \\ NCN & Named computation networking \\ NDN & Named data networking \\ NFaaS & Named function as a service \\ NFN & Named function networking \\ NFV & Network function virtualization \\ NIC & Network interface card \\ PIT & Pending interest table \\ QoS & Quality of service \\ RIB & Routing information base \\ RICE & Remote invocation in named function networking \\ SDN & Software-defined networking \\ SFC & Service Function Chaining \\ SSL & Secure socket layer \\ SVS & State vector synchronization \\ TCP & Transmission control protocol \\ TLV & Type-length-value \\ TLS & Transport layer security \\ \end{tabular}
## 7 Conclusions and Future Work  
This systematic review work focused on identifying the scope of RIP, its usability, performance, and reliability, by examining data from existing research. Experimental data from 40 studies were extracted and analyzed in a thorough investigation to achieve this purpose. RIP can be used as a non-invasive alternative to spirometry and enables the measurement of respiratory parameters of ambulatory subjects while sacrificing some accuracy. However, its usability beyond the lab environment enables the collection and interpretation of data while subjects engage in normal daily activities. It can be concluded that RIP is an acceptable method in some specific applications, while the technique requires further improvement to be used as a diagnostic tool. RIP has been studied for monitoring of respiratory volumes, detection of apnea in infants and adults, recognition of human activity through machine learning, and it can identify any respiratory event associated with respiratory insufficiency. RIP has the potential to be used for remote monitoring and diagnosis, as well as the detection and classification of specific human activities based on respiratory patterns through the application of machine learning.  
To our knowledge, this is the only paper that provides a systematically reviewed summary of the usage of RIP devices in the medical field. Collecting data from a large population of both healthy subjects and patients with illnesses affecting respiratory function could provide further insight into the performance and reliability of the RIP technique. There is scope for future research based on our observations, including but not limited to the development of RIP devices with better sensitivity and data processing techniques,self-calibrating methods that do not require ground truth, new methods for the integration of RIP devices into wearable garments that allow subjects to perform normal daily activities, portable RIP systems capable of storing respiratory function data and remote monitoring, and machine learning algorithms embedded in RIP systems capable of providing real-time diagnoses.  
M.S.R.: Investigation, methodology, formal analysis, writing--original draft. S.C.: Investigation, writing--original draft, formal analysis, visualization. M.R.: Conceptualization, methodology, resources, writing--review and editing, supervision. A.B.M.S.U.D.: Conceptualization, methodology, writing--review and editing, supervision. All authors have read and agreed to the published version of the manuscript.  
This research did not receive any external funding.  
This work is a systematic review; thus, no data were generated throughout the study.  
The authors declare no conflicts of interest.
## 2 Materials and Methods
### Gait Analysis: The G-STRIDE System  
The G-STRIDE device is based on the device presented in a previous paper [28]. The G-STRIDE device comprises an inertial sensor (IMU) and processing electronics that allow obtaining kinematic gait-related variables when the device is attached to the foot. Raw inertial data are stored in an SD memory card for subsequent off-line processing. These Raw IMU data (accelerations and angular rates) are post-processed for the estimation of spatio-temporal and cinematic parameters, which describe the gait pattern of each subject. The architecture of the G-STRIDE system for gait analysis can be separated into two development blocks: hardware for IMU data registration and software for gait analysis. The device is lightweight with dimensions 57 mm \(\times\) 27 mm \(\times\) 32 mm and is attached to the shoe with an elastic band during walking tests; the obtained variables are then stored in a database hosted in the Raspberry itself and are post-processed (removal of outliers, or steps not in straight line) (Figure 1).  
The variables estimated by the G-STRIDE are described in Table 1. The parameters are computed as the mean on a step-by-step basis, but we also estimate the standard deviation (STD).  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameter** & **Description** \\ \hline Total distance (m) & The total distance covered during a long free walk. \\ \hline Total time (s) & The total time spent in the free walk. \\ \hline Total steps & The total number of steps in the free walk. \\ \hline Gait cycle time (GCT) (s) & The time elapsed during a stride. \\ \hline Velocity (m/s) & The mean walking speed computed over the total detected \\  & steps measured in meters per second. \\ \hline Cadence (steps/min) & The number of steps per minute. \\ \hline Swing time (\% GCT) & Swing time (from toe-off to heel strike) as percentage of GCT. \\ \hline Stance-foot flat time (\% GCT) & Foot-flat (from start to end) time as percentage of GCT. It \\  & occurs between toe-strike and heel-off. \\ \hline Toe Off (\% GCT) & Percentage of the stride since the mid-stance until the foot is in the Toe Off instant, which corresponds to the Push Off phase. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Sensor-based gait estimated variables.  
Figure 1: G-STRIDE device attached to a participant’s foot.  
The database containing all the IMU recordings, the estimated parameters in Excel format, and the Python code to process the IMU signals are available in a Zenodo repository [43].
# Some Applications of ANN to Solar Radiation Estimation and Forecasting for Energy Applications
## 5 Estimation of Tilted Global Irradiation (GTI) from Horizontal Global Irradiation (GHI)
### Stationunarization of Solar Data  
Machine learning methods are efficient tools for forecasting time series with a stationary behavior. An MLP is a stationary model, which must use stationary data as input. To make solar irradiation time series stationary and to separate the climatic effects and the seasonal effects, the solar data are generally transformed in unitless variables called \"clearness index\", and denoted kt; kt is the ratio of the solar radiation on the earth, GHI, to that outside the atmosphere, EHI, and defined by Equation (6) [35]:  
\[kt=\frac{GHI}{EHI} \tag{7}\]  
It is the clearness index series, kt, that induces randomness, caused by the diversity of atmospheric components (dusts, aerosols, clouds motion, and humidity) on the solar irradiation measured at the Earth's surface.  
Numerous studies have showed that EHI can be efficiently replaced by the clear sky solar irradiation [22] taking into account the climatic conditions of the meteorological site; thus the clearness index is replaced by the clear sky index, k\({}_{\text{g,cs}}\), defined by:  
\[k_{g,cs}=\frac{GHI}{GHI_{CS}} \tag{8}\]  
with \(GHI_{CS}\) the global horizontal solar irradiation in clear sky conditions.  
For DNI, a similar index, k\({}_{\text{d,CS}}\)[47; 48], is defined by:  
\[k_{d,cs}=\frac{DNI}{DNI_{CS}} \tag{9}\]  
Various models of clear sky solar irradiations are available in the literature, which differ from each other mainly in the inputs needed by each model [49]. Solar irradiance models by clear sky denoted in the following clear sky models used meteorological variables (as ozone layer thickness, precipitable water, optical aerosol depth, etc.) and used solar geometry (solar elevation and air mass), using radiative transfer models to consider the absorption and diffusion effects of solar radiation into the atmosphere [50; 51]. The most widely used clear sky model is the Solis model developed by [PERSON] et al. [52] and simplified by [PERSON] [53], the European Solar Radiation Atlas (ESRA) model [54], and the Reference Evaluation on Solar Transmittance 2 (REST2) model [55].  
Thus, the simplified Solis clear sky model [53] was used here. It allowed calculations of the GHI\({}_{\text{CS}}\), and DNI\({}_{\text{CS}}\). This clear sky model was validated for each month by comparison with experimental solar radiation data measured in clear sky conditions. For illustration purposes, experimental and modelled solar irradiances by clear sky are plotted in Figure 9 for one day in April and in September.  
Figure 9: _Cont._