esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Background: In order to evaluate usefulness of a time-resolved spectroscopy (TRS) in screening test of cognitive dysfunction, we studied the effects of aging, cognitive dysfunction, brain atrophy on hemoglobin (Hb) concentrations and optical pathlengths (OPLs) in the prefrontal cortex (PFC) at rest, using TRS. Methods: Employing TRS, we measured Hb concentrations and OPLs at rest in the PFC, and evaluated the relationship between the TRS parameters and cognitive function assessed by Mini-Mental State Examination (MMSE). In addition, we evaluated the relationship between the TRS parameters and the brain atrophy assessed by MRI. Results: We found positive correlations between MMSE scores and oxygen saturation (SO\({}_{2}\)), oxy-Hb in the PFC, suggesting that the greater the degree of PFC activity, the higher the cognitive function. In addition, we found the negative correlation between the subject's age and SO\({}_{2}\) and oxy-Hb in the PFC, suggesting that the older the subject, the lower the PFC activity at rest. Moreover, the OPLs in the right PFC negatively correlated with degree of brain atrophy evaluated by MRI, indicating that the shorter the OPL, greater degree of brain atrophy. Conclusions: TRS allowed us to evaluate the relation between the cerebral blood oxygenation (CBO) in the PFC at rest and cognitive function.  
near infrared spectroscopy; aging; prefrontal cortex; TRS; magnetic resonance imaging; brain atrophy; VSRAD; optical pathlength; hemoglobin; cognitive function
## 3 Methodology  
As stated above, WSNs enable the collection of large amounts of data over wide areas but face challenges like the short lifetime of sensor nodes due to a reliance on batteries. Frequent battery replacement is cumbersome, while energy harvesting from sources like solar, though helpful, is variable and insufficient to meet high energy demands.  
Another major issue in WSNs is the hotspot problem, where an energy imbalance occurs due to the increased energy consumption of nodes located in data hotspots. In WSNs, multi-hop communication is typically used, where distant sensor nodes pass data to the BS through intermediate nodes rather than transmitting directly. This causes sensors in proximity to the BS to transmit and receive several data to relay packets from farther sensors. As a result, the nodes located near the BS (red nodes) experience higher energy consumption and a shorter lifespan compared to other nodes, as shown in Figure 1. When hotspot nodes die, the BS no longer receives their data or the data they should relay, leading to network isolation [14].  
Several solutions have been proposed to address energy limitations in WSNs, including WPT for direct node charging and the use of mobile chargers, such as UAVs, for short-distance wireless energy replenishment. Additionally, mobile sinks have been introduced to collect data from cluster heads rather than directly from all nodes, reducing the overall communication overhead. However, WPT suffers from low efficiency over long distances, mobile chargers are constrained by limited mobility and energy capacity, and while mobile sinks alleviate network congestion, they do not fully resolve the hotspot problem near cluster heads [25; 26; 27; 28; 29; 30; 31].
# A Haar Wavelet Decision Feedback Channel Estimation Method in OFDM Systems
## 3 Conventional Channel Estimation Methods  
### LS Channel Estimation  
The LS channel estimation method can be easy to implement in OFDM systems [11]. In LS channel estimation method, block type pilots are used to estimate the channel state information. It can be shown that the estimated channel frequency response (CFR) at pilots by the LS method is represented as  
\[\hat{\mathbf{H}}_{\text{LS}}(k_{\text{P}})=\frac{\mathbf{Y}(k_{\text{P}})}{\mathbf{X}(k_{ \text{P}})}, \tag{1}\]  
where \(\mathbf{X}(k_{\text{P}})\) and \(\mathbf{Y}(k_{\text{P}})\) are the transmitted and received pilot signal on the \(k_{\text{P}}\)-th pilot symbol, respectively. The channel coefficients of all data symbols in frequency-domain \(\hat{\mathbf{H}}_{\text{LS}}\) can be obtained by one-dimensional linear interpolation.
# DMS-YOLOv5: A Decoupled Multi-Scale YOLOv5 Method for Small Object Detection
## 3 Methods
### Decoupled Network Module  
The detection head network is mainly used to complete the classification of object categories and the discrimination of object position and predicted box size. In the YOLOv5 algorithm for one-stage object detection and the Faster R-CNN algorithm for two-stage object detection, the detection head network combines the two tasks of object classification and bounding box regression through a coupled network, completing both classification and regression operations simultaneously.  
Although the coupled network has advantages in improving the speed of the detection head network, due to the differences in the implementation principles of classification and bounding box regression tasks, improving the detection head network by decoupling can achieve branch processing for both tasks and improve the performance of both tasks. In the detection head network of the YOLOv5 algorithm, different sizes of coupled networks are used to predict objects of different sizes in the dataset. Based on the input of different image features, each input generates anchor boxes corresponding to the size of the object boxes in the dataset for the regression and positioning of predicted boxes. Each coupled network module contains a convolution module and a 1 \(\times\) 1 convolution module, and the feature map is directly fed into the coupled network to generate objects position and class information output. The structure diagram of a single coupled network is shown in Figure 8.  
Unlike the structure of the coupled network, the decoupled network divides the network into classification branch modules and bounding box regression branch modules according to the different functions of the tasks. The bounding box regression branch module is further divided into object confidence module and bounding box prediction module. The decoupled network extracts object position and class information separately and learns them through branch structures. The basic module of the decoupled network consists of a standard convolution module, a batch normalization layer, and a SiLU activation function. In addition, there is also a 1 \(\times\) 1 convolution module after the basic module. Finally, the classification module, object confidence module, and bounding box prediction module are  
Figure 8: Single Coupled network architecture diagram.  
combined in a concatenation manner to input three types of numerical information, namely classification loss function, confidence loss function, and bounding box regression loss function, to complete the classification and bounding box regression tasks. The structure diagram of the decoupled network is shown in Figure 9.
# A Review of Evaporite Beds Potential for Storage Caverns: Uncovering New Opportunities
## 3 Properties and Geomechanics of Bedded Evaporites
### Main Geomechanical Considerations in Bedded Evaporite Caverns
#### 3.3.3 Cyclic Loading
The results revealed an increased plastic strain in the cement sheath under cyclic loading. Cavern volume shrinkage can lead to cement debonding and gas leakage. A coupled analysis of cement sheath fatigue damage and rock salt creep was suggested for bedded salt caverns in [258]. The results indicated that the creep rate and volume shrinkage in bedded salts were lower than in pure rock salts, suggesting better wellbore integrity in such formations. It is important to note that pressure and temperature are key factors in three different damage types. The study in [259] found that creep rates increase significantly with rising temperatures. Higher internal pressure combined with shorter gas production times resulted in less cavern volume reduction in specific cases. The study in [260] investigated the influence of minimum, maximum, and average internal pressure on the stability of caverns during hydrogen storage via a numerical calculation model. An increase in average internal pressure caused an increase in tightness evaluation indexes such as leak range, hydrogen pore pressure distribution, and overall hydrogen leakage to the rocks in the vicinity of the cavern. A coupled thermo-mechanical model was proposed in [261] to investigate the effects of temperature and pressure changes during cyclic loading on cavern creep variations. The study in [262] coupled both pressure and temperature to understand their effect on tensile failure and ultimately cavern stability. Thermally induced tensile fractures due to high-frequency cycling were also seen in [263] through experiments and modeling. An integrated 1D and 3D geomechanical model was proposed in [264] to indicate the optimal internal pressure of caverns during hydrogen storage. Monitoring plays a critical role in observing changes in salt caverns, as they are inaccessible directly. Two indirect monitoring methods were proposed in [265], utilizing cavern pressure changes and liquid flow rate in open hole well sections. Several monitoring techniques, including geophysical tools and experiments, were described in [266]. Sonar survey, well pressure and temperature logging, cavern bottom sounding, micro-seismic monitoring, and subsidence survey as various methods of cavern monitoring were discussed in [267]. During integrity tests, factors such as thermal disequilibrium, brine permeation through cavern walls, additional salt dissolution, and transient creep must be considered to avoid misinterpretation of test results [268].
## 2 Materials and Methods
### Ionospheric Data Processing  
The ionosphere is monitored by the European Space Agency (ESA) Swarm constellation. The Swarm mission is composed of three identical satellites in low-Earth quasi-polar orbits, called Alpha, Bravo and Charlie, aiming to measure the geomagnetic field with state-of-the-art precision [21]. The use of Swarm data to study earthquake preparation has been explored by several researchers in the last few years by using a single case study approach [20; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31; 32; 33], or by statistically correlating the magnetic and electron density anomalies of Swarm data with M5.5+ earthquakes that occurred in the first 4.7 years of Swarm mission ([PERSON] et al. [34]), or the first eight years ([PERSON] et al. [35]), or by using machine learning ([PERSON] et al. [36]).  
In this report, we applied the MASS algorithm to Swarm magnetic field data defined with all the details in [29; 33; 34; 35], and the NeLOG algorithm defined with all the details in [29], which was used recently in [37] to study the 2020 Samos (Greece) earthquake. MASS analyses the data track-by-track removing the background, by estimating the derivative and subtracting a cubic spline. Such data processing also intrinsically removes the long-term trend of the geomagnetic field from the residuals. The residuals are monitored within a moving window comparing the root mean square of the window with the root mean square of the entire track along \(-50^{\circ}\)S and \(+50^{\circ}\)N geomagnetic latitude. Higher latitude values are excluded to avoid the influence of polar activity.  
NeLOG investigates the electron density latitudinal profiles, removing a polynomial of ten degrees from the investigated track and searching for tracks with at least ten anomalous residual samples. The sample is marked as anomalous if its value exceeds \"kt\" times the standard deviation of the whole residuals.
## 3 Results
### Regulations of the Biophysical Variables on WUE  
#### 3.3.1 The Correlation Between the WUE and Biophysical Variables  
The heatmap illustrates the correlations between WUE and various biophysical variables, including EVI, LAI, PPT, Rn, Temp, VPD, and VSWC in the FPENC over the period 2003-2022 (Figure 11), WUE showed a positive relationship with Rn, Temp, and VPD, and showed a negative relationship with EVI, LAI, PPT, and VSWC. Especially, WUE is strongly negatively correlated with LAI (\(-0.52\)), indicating that better vegetation health and density may not be significantly conducive to the improvement of WUE. There is an obvious positive correlation between WUE and VPD (0.52), suggesting the VPD would induce stomata closure and open, which dominate the variation of WUE. Conversely, VSWC showed a moderately negative correlation with WUE (\(-0.44\)), implying that drier soil conditions can enhance water use efficiency, highlighting the role of soil moisture in supporting plant growth and reducing water stress.  
Figure 11: The heatmap of the correlation between WUE and biophysical variables (EVI, enhanced vegetation index; LAI, leaf area index; PPT, precipitation; Rn, net radiation; Temp, air temperature; VPD, vapor pressure deficit; VSWC, volumetric soil water content) over the study period. The annual means (except for PPT, the annual totals of PPT were used) over 2003–2022 were used during the correlation plot.
# An Image Authentication Scheme Using Merkle Tree Mechanisms
## 2 Background
### Merkle Tree  
The Merkle tree was presented by [PERSON] in 1979 [14]. It is a useful tree structure for several application areas, especially in cryptography. Merkle trees have been an essential key to data verification throughout the history of computers. Their structure helps to verify the consistency of data content. Its architecture helps to speed up security authentication in big data applications. It is a complete binary tree, and each node is to hash the value from its child node. Merkle tree structure is shown in Figure 3[15; 16].  
Merkle trees are also a fundamental part of blockchain technology. For a block, the Merkle root comes from a hashing transaction and pairing two transactions to hash and generate the upper level tree node. By doing so, it will get one hash to store that is deterministic based on the hashes of all the underlying transactions. This single hash is called the Merkle root (Figure 4).  
In the blockchain, each block has a Merkle root stored in the block header. Merkle tree allows every node on the network to verify individual transaction without having to download and validate the entire block. If a copy of the block in the blockchain networks has the same Merkle root to another, then the transactions in that block are the same. Even a bit of incorrect data would lead to vastly different Merkle roots because of the properties of the hash. Therefore, it is not necessary to verify the amount of required information (Figure 5).  
Figure 4: The architecture of Merkle tree in the blockchain.  
Figure 3: Merkle tree structure.
###### Abstract  
The ability to discriminate between talkers based on their fundamental (F0) and formant frequencies can facilitate speech comprehension in multi-talker environments. To date, voice discrimination (VD) of children and adults has only been tested in quiet conditions. This study examines the effect of speech-shaped noise on the use of F0 only, formants only, and the combined F0 + formant cues for VD. A total of 24 adults (18-35 years) and 16 children (7-10 years) underwent VD threshold assessments in quiet and noisy environments with the tested cues. Thresholds were obtained using a three-interval, three-alternative, two-down, one-up adaptive procedure. The results demonstrated that noise negatively impacted the utilization of formants for VD. Consequently, F0 became the lead cue for VD for the adults in noisy environments, whereas the formants were the more accessible cue for VD in quiet environments. For children, however, both cues were poorly utilized in noisy environments. The finding that robust cues such as formants are not readily available for VD in noisy conditions has significant clinical implications. Specifically, the reliance on F0 in noisy environments highlights the difficulties that children encounter in multi-talker environments due to their poor F0 discrimination and emphasizes the importance of maintaining F0 cues in speech-processing strategies tailored for hearing devices.  
Keywords:voic discrimination; background noise; speech perception; F0; formants; temporal processing; spectral processing; school-age children +
Footnote †: journal: Applied
## 5 Therapeutic Implications of WA in AD  
WA is a secondary metabolite that is used to treat AD. An in vitro study demonstrated that _W. somnifera_ root contains WA, which has a significant effect on H\({}_{2}\)O\({}_{2}\) and A\(\beta\) amyloid-induced neurotoxicity [120]. In an AD model, cognitive defects induced by ibotenic acid that was significantly reversed by WA isolated from Ashwagandha root [121]. Oral administration of WA to mice caused a boost of AChE activity with improving muscarinic receptor binding abilities in the cortical area but did not affect the GABA, benzodiazepine, NMDA, or AMPA receptor in the globus pallidus and the lateral septum area of the brain. In contrast, WA reversed cholinergic marker reduction and enhanced binding affinity with the muscarinic receptor, suggesting that WA could be a potential compound for AD therapy [122]. On the other hand, the pro-inflammatory mediator iNOS (a) moderated A\(\beta\) nitration that ameliorated peptides inclination to aggregate nitration of A\(\beta\), (b) enhanced the peptide's propensity to formed seeding core and aggregated A\(\beta\) plaques, (c) inhibited iNOS expression and NF-\(\kappa\)B signaling pathway, and (d) diminished aggregation of A\(\beta\) plaques in the cerebral area and flourished cognitive performance [123; 124].  
Similarly, WA inhibited NF-\(\kappa\)B and iNOS markers in astrocytes [125]. Furthermore, Hsp90 accelerates aberrant accumulation and aggregation of \(\tau\) protein [126] and induces A\(\beta\) toxicity in AD [126]. In addition, WA inhibited Hsp90 [127] and induced Hsp 27 and Hsp70 expressions, and reduced \(\tau\) aggregation in a mouse model of tauopathies [128; 129]. Cholinesterase enzyme develops AD, which was inhibited by WA obtained from the water extract of Ashwagandha [130]. It inhibited nuclear factor NF-\(\kappa\)B activation, which is responsible for the expression of oxidation and inflammation gene, thus prevented the accumulation of A\(\beta\). WA also increased the neuro-protective protein heme oxygenase-1, which is beneficial to AD prevention [131; 132]. WA additionally enhances memory [133], prevents A\(\beta\) production, reconstructs synapses, and regenerates axons [134]. AD is often related to the hyperactivity of microglial cells. In this respect, WA modulated their action to prevent brain tissue damage [135]. In this respect, an in vitro study demonstrated that WA inhibits A\(\beta\) production in microglial and SH-SY5Y cells overexpressing amyloid precursor protein (SH-APP cells) [135] and suppresses NF-\(\kappa\)B mediated neuro-inflammation [136]. Multidomain protein LRRK2 remains often mutated, which elevates kinase expression to enhance AD, regulated by WA [137; 138]. [PERSON] et al. (2012) demonstrated that the TAR DNA-binding protein-43, a pathological hallmark of AD, is alleviated by WA and boosted autophagic marker LC3 BII level in the brain, while it suppressed NF-\(\kappa\)B-dependent neuro-inflammation in frontotemporal lobar degeneration (FTLD) in a mice model [27].
## 4 Evaluation
### Evaluation Settings
The criteria to determine the correct answers were set to be consistent with the baseline of the DCASE 2020 Task 4 [34]. Moreover, the performance of each class was micro-averaged, which aggregated the contributions of all the test samples regardless of the class, and macro-averaged, which averages the individual performance of each class.
## 2 Materials and Methods
### RNA Extraction, Reverse Transcription and Quantitative Real-Time PCR  
RNA was extracted using the ReliaPrep(tm) mRNA Cell and Tissue Miniprep System (Promega, Waldorf, Germany). Total RNA (500 ng) was reverse transcribed using random primers with and without GoScript(tm) Reverse Transcriptase (Promega, Waldorf, Germany). The qRT-PCR was carried out using 2 uL cDNA, 300 nM of forward and reverse primer and SsoFast(tm) EvaGreen(r) Supermix (Bio-Rad, Feldkirchen, Germany) on MaxPro-Max3000P Real-time PCR system (Stratagene, La Jolla, CA, USA). Reaction conditions were as follows:95 \({}^{\circ}\)C for 10 min, 40 cycles of 10 s at 95 \({}^{\circ}\)C and 20 s at 60 \({}^{\circ}\)C, and followed by a melting curve stage of 95 \({}^{\circ}\)C for 10 s and 60 \({}^{\circ}\)C for 20 s. The relative gene expression of insulin (_INS_), glucagon (_GCG_), pancreatic and duodenal homeobox 1 (_PDXI_), Neurogenin 3 (_NGN3_), NK6 Homeobox 1 (_NKX6.1_) and aldolase fructose-bisphosphate B (_ALDOB_) were measured by reverse transcription quantitative polymerase chain reaction (RT-qPCR) normalized against the expression level of the glyceraldehyde 3-phosphate dehydrogenase (_GAPDH_) gene via threshold cycle (Ct) value based on the comparative 2\({}^{-\Delta\Delta\)Ct method. No template was used as reverse transcription (RT) control. Results were expressed as a fold change compared to NPICCs cultured under basal conditions.  
The mRNA expression of _GPR43_ and _GPR41_ was analyzed by PCR using T100 Thermal Cycler (Bio-rad, Feldkirchen, Germany) with the following program: 95 \({}^{\circ}\)C for 3 min, 45 cycles of 15 s at 95 \({}^{\circ}\)C and 20 s at 60 \({}^{\circ}\)C, 1 min at 72 \({}^{\circ}\)C, and finally 5 min at 72 \({}^{\circ}\)C. The PCR products were evaluated by 1.5% agarose gel electrophoresis in Tris-borate-EDTA buffer stained with ethidium bromide (Promega, Waldorf, Germany). Images were taken by Gel Doc Imaging System (Intas Science imaging, Gottingen, Germany). Amplification without reverse transcriptase (\(-\)) RT served as control. Primer sequences are listed in Table 1.