esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Effects of DSW-Derived Mineral Waters on the Expression of TJ-Related Proteins  
To clarify the mechanism underlying the inhibition of paracellular transport across mineral water-pre-treated Caco-2 monolayers, the expression of TJ-related proteins was investigated at the mRNA level. Occludin mRNA expression was significantly reduced by LPS treatment but was significantly increased by treatment with HMLS water. TM, HM, and HMHC groups showed decreased occludin mRNA expression (\(p<0.05\); Figure 2a). Zonula occluding (ZO)-1 mRNA expression was slightly, albeit not significantly, decreased by LPS treatment. HMLS water induced a 30-fold increase in ZO-1 mRNA expression compared to the level in the CON group, whereas TM, HM, and HMHC groups showed decreased ZO-1 expression as compared with the CON group (\(p<0.05\);  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Sample**} & \multicolumn{4}{c}{**Mineral Contents (ppm)**} & \multirow{2}{*}{**Na/Mg**} & \multirow{2}{*}{**Mg/Ca**} \\ \cline{2-2} \cline{5-6}  & **Na\({}^{+}\)** & & & & **Mg\({}^{2+}\)** & **K\({}^{+}\)** & **Ca\({}^{2+}\)** \\ \hline DSW & 10,700 & 1380 & 480 & 390 & 7.75 & 3.5 \\ TM & 29.5 & 6.7 & 1.7 & 2.4 & 4.4 & 2.8 \\ HM & 39.9 & 248 & 45.2 & 0.78 & 0.16 & 318 \\ HMLS & 36 & 179 & 1.7 & 41.5 & 0.2 & 4.3 \\ HMHC & 133 & 211 & 48 & 200 & 0.63 & 1.1 \\ \hline \hline \end{tabular} DSW (deep sea water), TM (trace mineral), HM (high magnesium), HMLS (high magnesium low sodium), and HMHC (high magnesium high calcium).  
\end{table}
Table 1: Main mineral contents of water samples prepared by dialysis of DSW and ion addition.  
Figure 1: Effects of DSW-derived mineral waters on TEER and permeability: **(a)** time course analysis of TEER in a Caco-2 monolayer treated with the indicated mineral waters and LPS; **(b)** TEER values of the treatment groups at 48 h; and **(c)** effects of mineral water samples on FITC-dextran permeability. Caco-2 cells were cultured on the apical side of a Transwell insert and Raw 264.7 cells were seeded on the basolateral side. After 24 h, the medium was exchanged for medium containing the water samples, LPS (2 ug/mL) was added to the basolateral side, and the plate was incubated for 48 h. Different letters above the bars indicate significant differences ([PERSON]’s multiple range test; \(p<0.05\)). The letters are the order of values from small to large.  
Figure 2b). JAM-1 mRNA expression was slightly, albeit not significantly, increased by LPS treatment, but was significantly increased by treatment with HMLS water. JAM-1 mRNA expression was slightly reduced in the TM and HMHC groups, and slightly increased in the HM group when compared with the LPS group (\(p<0.05\); Figure 2c). Claudin-1 and -3 mRNA levels were significantly reduced by LPS treatment, but expression was rescued upon HMLS treatment, whereas the other waters had no effect (\(p<0.05\); Figure 2d,e). Claudin-4 mRNA expression was not affected by LPS treatment and was only significantly increased upon treatment with HMLS water (\(p<0.05\); Figure 2f).
## 6 Experimental Results and Analysis
### Experimental Results of the Prediction and Association  
In this experiment, the following metrics provided in the MOT Challenge Benchmark are used to evaluate the performance of prediction and association. (1) Multi-object tracking accuracy (_MOTA_):  
\[MOTA=1-\frac{\sum\limits_{t=1}^{num}FN_{t}+FP_{t}+IDSW_{t}}{\sum\limits_{t=1}^{num }GT_{t}}. \tag{15}\]  
where \(num\) is the frame number of the sequence, and \(GT_{t}\) and \(IDSW_{t}\) are the number of ground-truth objects and the number of ID switches for all objects in the \(t\)th frame of the sequence, respectively.  
(2) Multiple object tracking precision (_MOTP_)  
\[MOTP=\frac{\sum\limits_{t=1}^{num}\sum\limits_{i=1}^{c_{t}}IoU_{i,t}}{\sum \limits_{t=1}^{num}c_{t}}, \tag{16}\]  
where \(c_{t}\) is the number of objects associated successfully in the \(t\)th frame, and \(IoU_{i,t}\) is the bounding box overlap of the \(i\)th successfully associated object with the ground-truth object in the \(t\)th frame.  
In Equation (9), the threshold \(T_{a}\) for the aircraft and \(T_{v}\) are set at 0.95 and 0.9, respectively. We select eight sets of video sequences from the dataset for experiments. These sequences consist of four types of KMN: _in-block_, _off-block_, _docking stairs_, and _undocking stairs_. The results of the quantitative assessment for eight test samples are given in Table 5. The experimental results show that the sub-module of the prediction and association used in our framework has only 33 ID switches owing to target association errors, whereas there are a total of 8397 objects detected. Specifically, the association success rate is high, with the average _MOTA_ of 95.09%, and the localization accuracy is high, with the average _MOTP_ of 92.63%.  
Figure 10 shows several correlation results for qualitative analysis. The results for _in-block_ reveal that the appearance of the aircraft varies considerably due to the number of turns required during _in-block_, but the aircraft ID number remains constant throughout this process. Similar results for _off-block_, _docking stairs_, and _undocking stairs_ indicate that our method maintains association robustness in response to deformation. In Figure 10b, there are multiple targets such as craters, tractors, and aircraft. However, the ID of each target remains the same, indicating that the sub-module of the prediction and association remains robust against the complex background of the airport ground.
# A Design of a Parcel Delivery System for Point to Point Delivery with IoT Technology
## 2 Background: Underlying IoT Technology  
To implement IoT services, various elements such as devices, networks, application servers, and modules are needed. There is a service called IoT platform that helps to use these devices efficiently [12]. IoT platform refers to services that can meet various components and easily combine and increase utility value by connecting each element. Typically, Qualcomm's AllJoyn, Microsoft's Azure IoT Suite, and SKT's ThingPlug offer centralized services [13; 14]. There are so many IoT connection protocols such as Sigox, LoRa, LTE-M, NB-IoT, Cat-M1 [15], and so on. Although it can be used up, SKT's ThingPlug is used as an IoT platform in this paper. ThingPlug supports Lora, Cat-M1, LTE-M, and LTE. Geopositioning data are low-capacity data and real-time is not strictly required. Thus, we found low speed and power efficient communication and it was LoRa [16; 17].
# Integrated Communication and Measurement System with BOC-Assisted OFDM
## 2 The Transmitting and Receiving System for the Composite Signal
### Signal Model
Based on the BOC signal characteristics of spectrum symmetry and being located on both sides of the central frequency, only the main lobe of the BOC signal exists in the subcarriers (blue subcarriers), namely, \(S_{k}(1)\), \(S_{k}(2)\), \(S_{k}(15)\), \(S_{k}(16)=0\). The OFDM signal and the side lobe of the BOC signal are superposed in the remaining subcarriers (red subcarriers) in according with their power ratios. The spectrum of the composite signal in this paper is shown in Figure 4.
## 2 Fundamentals of Terahertz Communication  
### The Terahertz Spectrum  
The THz frequency range is approximately in the range from 0.1 THz to 10 THz and is between the microwave and optical frequency ranges [4; 10]. It provides a bandwidth  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Sr. No.** & **Microwave Communication [17; 18]** & **Optical FSO Communication [1; 7]** & **THz-FSO Communication [5; 19]** \\ \hline
1 & Bandwidth is limited to the GHz range. Suitable for long-distance links but offers lower data rates. & Moderate bandwidth (100s of GHz) with higher data rates compared to microwave systems. & Extremely high bandwidth (THz range), enabling data rates beyond 1 Tbps. \\ \hline
2 & Minimal atmospheric attenuation ensures stable links even under adverse conditions. & Moderate attenuation due to environmental factors like fog and rain. & High atmospheric attenuation caused by water vapor absorption. \\ \hline
3 & Long transmission distances are supported by lower frequencies and minimal loss. & Medium-range communication is suitable for urban and controlled environments. & Short to medium-range communication requiring advanced alignment mechanisms. \\ \hline
4 & Cost-effective due to well-established hardware technologies. & Higher hardware costs are driven advanced optical components and alignment systems. & High costs due to nascent THz components like QCLs and high-power detectors. \\ \hline
5 & Security is moderate due to wide beam propagation, making interception easier. & High security with narrow beamwidths reducing interception risks. & Very high security due to highly directional and narrow beams. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of Communication Technologies.  
capability higher than 100 GHz, which is higher than the microwave system, which operates in a range from MHz up to a few GHZ only [16; 20]. This tremendous transmission capacity, coupled with the ability to provide data rates of over 1 Tbps, makes the THz spectrum an important foundation for future wireless communications, and equally so for the envisaged 6G networks [21; 22].
## 2 Data Acquisition and Processing  
The principal data examined in this study include ship AIS data, sensor data, and meteorological and sea condition data. The meteorological and sea condition data are collected hourly with a spatio-temporal resolution of 0.25\({}^{\circ}\)\(\times\) 0.25\({}^{\circ}\). AIS and sensor data are processed to match this spatio-temporal resolution through aggregation and interpolation.
# Analysis of Electromagnetic Field Characteristics of Wave Glider
###### Abstract  
A wave glider is an ocean observation platform that utilizes wave energy to drive and solar energy to power. Its metal structure will generate related electromagnetic fields due to corrosion and underwater motion. In the detection of weak electromagnetic field signals underwater, its own electromagnetic field characteristics will have an impact on signal detection. To study the applicability of electric field sensors and magnetic field sensors on wave glider platforms, the structural characteristics of the wave glider were analyzed, and the installation positions of electric field sensors and magnetic field sensors were designed based on the different motion states of the water surface mother body and underwater towing body. The measured electromagnetic field data of the wave glider platform were measured, and the measured data were analyzed. It was determined that the interference electric field energy under typical working conditions of the wave glider was mainly concentrated within 1 Hz, which decreased with increasing frequency, and the magnitude was mV/m. The magnitude of the interference magnetic field is several tens of nT, indicating that the electromagnetic field interference is significant during the working state of the wave glider. Installing an electric field sensor directly at the bottom of the wave glider will cause significant noise interference, while installing the magnetic field sensor directly at the bottom of the tractor will affect the servo and the shaking-induced magnetic field. Moreover, wave gliders should not use electric field signals below 1 Hz as signal sources, but they can utilize axial frequency electromagnetic fields to detect weak electromagnetic signals underwater.  
wave glider; underwater electromagnetic field; target detection; applicability research; electromagnetic characteristic analysis +
Footnote †: journal: Applied
## 5 Analysing Feature Fusion Performance According to Kappa-Error Diagrams
### Analyzing Feature Fusion Performance for AU Prediction Based on Kappa-Error Diagram Evaluation  
Extracting a power feature not only reflects the essential characteristic of AUs, but also enhances the discriminative performance for AU prediction. Considering the differences of AUs' images, no feature displays stable description for all images. Actually, different features present different representational performance when describing the different objects or regions.  
This paper designs a fusion experiment analysing the feature description ability for different AUs. Here, we chose four features (Gabor/geometry/LBP/WLD) for representing AU regions. Four-features extraction is done on the same public dataset at the same time. Three kinds of classifiers (MLP/SMO/IBK) were employed to train and learn AUs' features separately. Therefore, the corresponding kappa and error values are accounted for in each feature pair in predicting one AU region with one classifier. For four features, there are six pairs of feature combination. In Figure 4, three colours (red/green/blue) represent three kinds of classifiers, while six different symbols represent six feature pairs. Figure 4 shows the fusing results of six feature pairs for each AU prediction. The horizontal and vertical axes are the diversity parameter (kappa) and the average error parameter (error) respectively. The boundary curve of \(K\) = \(1-1/(1-e)\) is shown in the diagram.  
According to Figure 4, fusion performance of six feature pairs varies for different AUs' prediction. For the glabella region (AU10) and the middle region of face (AU4), feature combinations have high performance on AU recognition. The average error rate is generally lower than 0.7. Furthermore, diversity between four features exists in these areas. Additionally, the evaluation points are distributed close to the boundary, which indicates that the corresponding feature pair has good fusion characteristics. For eyes and mouth regions (AU2/AU13/AU15/AU16/AU24/AU27), the average error rate of feature pairs is about 0.7 and the kappa value in most situations is higher than others, which illustrates that the diversity of feature pairs in describing these regions is relatively weak compared to the above two regions. It also could be found from Figure 3 that the Gabor and LBP feature combination, denoted by \"+\", shows a better fusion performance than others. However, no other feature pairs display an overall advantage for all AUs' prediction. Four features show different representing ability in different AU regions, and fusion of any two features has a variety of representation performance. In conclusion, this paper adopts all four features to describe each AU and fuses them to enhance the overall describing ability for all selected expression AUs.
## 4 Results
### Collocations of HY-2B/C/D and SMAP
#### 4.1.2 Tropical Cyclones Jointly Captured by HY-2 and SMAP  
In order to count the number of typhoons jointly captured by the two satellites, the ability of SMAP to capture typhoons in 2019-2021 is introduced first. According to the location of tropical cyclones at each time, the global ocean is first divided into the southern hemisphere and the northern hemisphere, and then the northern hemisphere is further divided into the Atlantic Ocean, the Eastern Pacific Ocean, the Central Pacific Ocean, the Western Pacific Ocean, the Northern Indian Ocean, and the southern hemisphere ocean, respectively. However, the tropical cyclone of hurricane Ema in 2019 was captured by SMAP. Therefore, the assessment of HY-2 in this study does not include the Central Pacific Ocean.  
Table 3 gives the statistics of tropical cyclones captured by SMAP during the period 2019-2021. There are 41, 45, 29, 11, and 64 tropical cyclones observed by SMAP in the Atlantic Ocean, Western Pacific Ocean, Eastern Pacific Ocean, Indian Ocean, and southern hemisphere, respectively. During these three years, SMAP observed the most tropical  
Figure 2: Global distribution of collocations between HY-2B/C/D series scatterometers and the SMAP radiometer under tropical cyclone conditions during 2019–2021 (0.25\({}^{\circ}\times\) 0.25\({}^{\circ}\) grid). (**a**) Number of collocated HY-2B/C/D scatterometers and the SMAP radiometer; (**b–d**) are the same for HY-2B, HY-2C, and HY-2D, respectively.  
cyclones during 2020, and among the six basins, the southern hemisphere contained the largest number of observed tropical cyclones. As shown in Table 3, the number of captured storms is the smallest in the Northern Indian Ocean, which is consistent with the low frequency of occurrence in this area.  
After the HY-2 scatterometer data are matched with SMAP, the number of tropical cyclones observed by the two satellites at the same time is also counted, as shown in Table 3. The local times of the ascending/descending node of the HY-2 satellite and SMAP are the same, as shown in Table 1, providing a good opportunity to match the data for instrument inter-calibrations. Since HY-2B (3 July 2019-31 December 2021 for evaluation) was launched the earliest, it has also captured the most tropical cyclones (Figure 2a and Table 3) relative to HY-2C (25 September 2020-31 December 2021 for evaluation) and HY-2D (24 June 2021-31 December 2021 for evaluation), which is consistent with Figure 2b-d.
# Blind Compensation of \(I/q\) Impairments in Wireless Transceivers
###### Abstract  
The majority of techniques that deal with the mitigation of in-phase and quadrature-phase (\(I/Q\)) imbalance at the transmitter (pre-compensation) require long training sequences, reducing the throughput of the system. These techniques also require a feedback path, which adds more complexity and cost to the transmitter architecture. Blind estimation techniques are attractive for avoiding the use of long training sequences. In this paper, we propose a blind frequency-independent \(I/Q\) imbalance compensation method based on the maximum likelihood (ML) estimation of the imbalance parameters of a transceiver. A closed-form joint probability density function (PDF) for the imbalanced \(I\) and \(Q\) signals is derived and validated. ML estimation is then used to estimate the imbalance parameters using the derived joint PDF of the output \(I\) and \(Q\) signals. Various figures of merit have been used to evaluate the efficacy of the proposed approach using extensive computer simulations and measurements. Additionally, the bit error rate curves show the effectiveness of the proposed method in the presence of the wireless channel and Additive White Gaussian Noise. Real-world experimental results show an image rejection of greater than 30 dB as compared to the uncompensated system. This method has also been found to be robust in the presence of practical system impairments, such as time and phase delay mismatches.  
cumulative distribution function; demodulator; direct conversion transceivers; \(I/Q\) imbalance; modulator +
Footnote †: journal: Sensors
# Exploring Vitamin D Synthesis and Function in Cardiovascular Health: A Narrative Review
## 4 Vitamin D Deficiency and Cardiovascular Risk  
Vitamin D deficiency is associated with many health consequences, including skeletal problems (i.e., rickets, fractures, osteomalacia, osteopenia) and non-skeletal problems [24; 25]. Non-skeletal complications include heart disease and complications such as systolic and diastolic dysfunction, abdominal aortic aneurysm, and hypertension [26; 27; 28; 29; 30; 31]. Vitamin D deficiency is associated with several cardiovascular risk factors. By increasing the synthesis of renin and angiotensin II, Vitamin D deficiency can induce the production of reactive oxygen species (ROS). Furthermore, being implicated in the processes of calcification and proliferation of smooth muscles, Vitamin D levels directly influence cardiovascular health [32]. A study from the Intermountain Integrated Health System prospective database of 41,504 patients showed an association between Vitamin D deficiency and an increased risk of peripheral vascular disease (PVD), heart failure and coronary heart disease/myocardial infarction, and stroke [33]. Furthermore, following the Hill criteria, low serum 25(OH)D levels have been associated with the risk of cardiovascular disease [34]. Results from a meta-analysis involving 65,994 individuals also show a linear and inverse relationship between Vitamin D levels and the risk of cardiovascular disease [35]. Recent studies demonstrate that Vitamin D can regulate the expression of specific metalloproteinases and their inhibitors, which promote the development of heart failure [36]. Finally, growing evidence reveals the anti-inflammatory activity of Vitamin D in inhibiting the activation of tumor necrosis factor-alpha (TNF-\(\alpha\)), nuclear factor kappa B (NF-kB), and interleukin-10, all known important contributors of cardiovascular disease [37].
# Centralized vs. Decentralized: Performance Comparison between BigchainDB and Amazon QLDB
## 3 Overview of Centralized and Decentralized Ledgers
### Overview of Decentralized Ledgers  
A decentralized ledger is defined in [21] as a set of nodes, each storing a local copy of the entire ledger. According to [22], decentralized ledgers are a specific data structure maintained by multiple parties via a consensus protocol, with each party preserving a copy of the ledger.  
Decentralized ledgers can be roughly divided into two main categories: public ledgers and authorized ledgers [21]. Public ledgers are low-cost, secure, trusted and designed to support the decentralized record keeping of financial transactions (e.g., settlement payments) made within large groups of vendors who do not necessarily know or trust each other [23].  
In the case of distributed ledger technology, the entities that collectively operate the distributed platform can use the authentication process in a decentralized manner. The platform's resources are tamper-resistant (thus not allowing for hidden manipulation of data) and highly available (given the collectivist nature of the platform) [21]. The nodes form a decentralized database (the ledger) that does not depend on a single authority or point of validation. Instead, each node can add data (digital transactions of various types, although limitations do apply to the size of the data block involved in the transaction) to their local ledger, after validation. Each added transaction will then be part of the new validation process for new transactions, ensuring the immutability of the old transactions. The nodes are cryptographically signed and cross-validated, thus allowing them to be used in a zero-trust manner (unsecure environments where malicious actors can try to alter given data) or in unstable environments (generating faults in the network, crashing nodes etc.), generally recognized as unpredictable Byzantine faults [24]. In this case, we can define decentralized ledgers as a specific data structure maintained by multiple parties via a consensus protocol, with each party holding a copy of the ledger [22].  
In this approach, given that the nodes can store different versions of the ledger (accounting for the internal states of the faulted nodes), inconsistencies can be solved via various mechanisms, the most common being the consensus [7]. Depending on the ledgernetwork architecture, the nodes that are involved in consensus finding can be a selection of the available nodes (in permissioned DLT protocols), while in other protocols, all nodes can be used in finding consensus.  
The transparency of the public ledgers for all system participants constitutes a critical feature [25; 26]. As a result, the public ledger transactions are immutable, transparent and visible to all entities [27]. In contrast, adding and reading transaction records in authorized ledgers is restricted to specifically designated parties [28]. Authorized ledgers are also known as private blockchains because they frequently require the participant's authentication and authorization for the permission level of blockchain participants [29].
## 4 Performance Analysis  
In this section we define the expressions of the performance metrics, _i.e._, throughput efficiency and energy efficiency, which is used to analyse the performance of the C-HARQ protocol against the CARQ and S&W ARQ protocols.