esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Air Analysis
#### 3.2.2 Air Quality Analysis for Selection Cases (SCs)  
Figure 8 shows the monthly wind direction frequencies in 16 directions for SCs. The southerly direction was dominant in April, May, June, and July; however, the main wind direction did not appear in other periods. This is because the Northeast Asia region, including South Korea, is an Asian monsoon region with clear seasonal changes in wind  
Figure 6: Correlations between heavy metals in all cases (ALL) and SCs.  
Figure 7: Correlations between iron (Fe) and manganese (Mn) in ALL (**left**) and SCs (**right**).  
direction, andoutheasterly winds occur in summer under the influence of high pressure in the North Pacific [64].  
In SCs, PM2.5 and PM10 were 26.54 \(\mathrm{\SIUnitSymbolMicro g}\)/m\({}^{3}\) (25.78-27.31 \(\mathrm{\SIUnitSymbolMicro g}\)/m\({}^{3}\), 95% CI) and 45.84 \(\mathrm{\SIUnitSymbolMicro g}\)/m\({}^{3}\) (44.26-47.43 \(\mathrm{\SIUnitSymbolMicro g}\)/m\({}^{3}\), 95% CI), respectively, which were higher than the annual means (Figure 9 and Table S5). In particular, in SC, the concentrations of the metal components were higher than the total mean concentrations.  
Figure 8: Number of wind directions for each month in SCs (see Table S4 for more detail).  
Figure 9: _Cont._  
By component, the concentrations of SO4\({}^{2-}\) and NH3\({}^{+}\) were the highest in summer (6.41 \(\upmu\)g/m\({}^{3}\) (23.98%), and 3.31 \(\upmu\)g/m\({}^{3}\) (11.83%), respectively), whereas NO3\({}^{-}\) concentrations were the highest in spring (3.52 \(\upmu\)g/m\({}^{3}\) (11.64 %)). When analyzed by their proportions in PM2.5, the summer concentrations of SO4\({}^{2-}\) and NO3\({}^{-}\) were similar to those reported in a previous study of Ulsan industrial complexes in 2003 (7 \(\upmu\)g/m\({}^{3}\) (27.2%) and 3-11.1 \(\upmu\)g/m\({}^{3}\) (17.4-25.4%), respectively), and NH4\({}^{+}\) concentrations were higher (1.6 \(\upmu\)g/m\({}^{3}\) (6.2%)) [27]. However, our findings are similar to the results in the Ulsan downtown area in 2008-2009 reported in another study: 30.2 \(\upmu\)g/m\({}^{3}\) for PM2.5, 7.1 \(\upmu\)g/m\({}^{3}\) (23.5%) for SO4\({}^{2-}\), 2.6 \(\upmu\)g/m\({}^{3}\) (8.6%) for NH4\({}^{+}\), and 3.0 \(\upmu\)g/m\({}^{3}\) (9.9%) for NO3\({}^{-}\)[36].  
The OC concentration was 4.09 \(\upmu\)g/m\({}^{3}\) (all cases: 2.91 \(\upmu\)g/m\({}^{3}\)), and the EC concentration was 0.99 \(\upmu\)g/m\({}^{3}\) (all cases: 0.62 \(\upmu\)g/m\({}^{3}\)). Both OC and EC concentrations were higher than the annual means. Both OC and EC showed high concentrations in winter. The OC/EC ratio was 4.04 in winter and 4.41 in spring, indicating small changes.
## 4 Experiments and Results
### Experimental Setup  
#### 4.1.1 Dataset Building  
To verify the effectiveness of the proposed method, we built a new knowledge-based QA dataset and evaluate the proposed approach. It is described briefly as follows:  
Knowledge-based QA: The dataset is a self-built Chinese knowledge-based QA with 24k QA pairs corpus extracted from multiple resources, including various websites. The question words employed are mainly \"what\", \"why\", and \"how\" for non-factoid questions. We also added multiple QA pairs about healthcare from cMedQA, which is collected from an online Chinese medical question answering service. Since the knowledge-based QA contains numerous aspects of knowledge questions and answers, it is possible to verify the feasibility of our model in the answer selection. The dataset is characterized as shown in Table 1. The correct answer is marked in bold, among which 80% are training data and 20% are test data. The medical QA pair is shown in Table 2.  
The knowledge-based text is named as knowledge.txt, which is shown in Table 3. Before testing, the knowledge-based test was applied by our model to learn the background of the QA pairs.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Question** & **Answers** \\ \hline \multirow{5}{*}{What is the main reason why fog tends to form on clear nights from late fall to early spring of the following year?} & Right: Atmospheric inverse radiation is weak on clear nights, and cooling near the ground is rapid. \\  & False: The amount of water vapor in the atmosphere is high on clear days. \\ \cline{1-1}  & False: Evaporation of water vapor from the ground is strong on clear nights. \\ \cline{1-1}  & False: There is less condensed nuclei material in the clear atmosphere. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Example of knowledge-based QA.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Question** & **Answers** \\ \hline \multirow{5}{*}{After touching the small animals at the zoo yesterday, I had itchy skin and an allergic reaction. What should I do?} & Right: Hello. Your situation is a manifestation of a skin allergy. The most common treatment is anti-allergy treatment, such as taking \\  & Ioratadine vitamin c and ketofien. You also \\ \cline{1-1}  & Ioratadine vitamin c and ketofien. You also \\ \cline{1-1}  & & need to drink more water and do not eat \\ \cline{1-1}  & & spicy food. \\ \cline{1-1}  & & False: Measles and chickenpox are two \\ \cline{1-1}  & & different diseases. For example, if measles is \\ \cline{1-1}  & & prevalent in the area, vaccination is \\ \cline{1-1}  & & recommended. \\ \hline \hline \end{tabular}
\end{table}
Table 2: Example of a medical QA pair in knowledge-based QA.
## 2 Materials and Methods
### Outcome Measures  
The primary outcome was the pre-activation time of the knee stabilizer muscles of the dominant leg: rectus femoris (RF), vastus medialis (VM), biceps femoris (BF), and medial hamstrings (MH) were evaluated through sEMG. We placed the electrodes on the dominant leg [41; 42], according to the recommendations of the sEMG recommendations for Not-Invasive Assessment of Muscles (SENIAM) [44]. The knee stabilizer muscles' time of pre-activation is computed as the interval of time between the beginning of muscle activation and the contact with the floor, evaluated by sEMG (Freze EMG 1000; BTS Bioengineering Spa, Garbagnate M.se-Milano, ITA), through 80 mm bipolar surface electrodes (Ambu, Neuroline, Ballerup, Denmark) [45]. Raw sEMG sample recording frequency was 1000 Hz. An IMU (G-sensor, BTS Bioengineering Spa, Garbagnate M.se-Milano, ITA) provided kinematic data. We positioned the elastic band containing the IMU on lumbar vertebrae (L5). IMU signal provided the exact time of floor contact that we used as reference to calculate muscle pre-activation. These signals were elaborated using sEMG Analyzer software (BTS Bioengineering Spa, Garbagnate M.se-Milano, ITA) through the \"drop fall\" protocol [46]. Each athlete was asked to move vertically, without controlling the land,from a 32 cm platform. All these evaluations were performed by a physician with a 5-year expertise in sEMG measurement (Figure 1).  
Body composition was assessed as secondary outcome, through the BIA (Akern s.r.l, Montacchiello, Pisa, Italy) evaluating: body cellular mass-BCM, fat mass (%), fat mass weight (kg), free fat mass weight (kg), muscle mass (kg), extra-cellular mass-ECM (kg), ECM/BCM ratio, phase angle (\({}^{\circ}\)), total body water (kg), intracellular water (kg), extra-cellular water (kg), O2 consumption (mL/min), and basal metabolic rate (kcal). Two low-impedance electrodes were positioned on the right hand and on the corresponding foot.  
Lastly, we assessed the power of the lower limb muscles, through counter movement jump (CMJ), and squat jump (SJ) tests. CMJ assessed the ability to rapidly generate power in stretch-shortening cycle activities, while the SJ assessed the ability to quickly produce power in concentric movements [47].  
More in detail, we considered the functional tests at the baseline (T0), 8 days before the COVID-19 positive nasopharyngeal swabs, and at return-to-training (T1) 3 days after the COVID-19 negative conversion at the RT-PCR test (see Figure 2 for further details).
## 3 Results
### Yield Estimation Accuracy during the N Application Growth Stages  
This experiment used synthetic data to test whether crop N status and yield could be robustly estimated using observations of LAI during critical growth stages for crop management. The results show that yield estimates from constraining DALEC-Crop with LAI observations during the period of recommended N fertilization had a NRMSE of 14% (Experiment 3; Figure 8). From a linear fit the model had a high agreement with the observed yield (R\({}^{2}\) = 0.97). Based on the yield residual error (i.e., measured--modelled), which ranged from \(-\)0.52 to 1.68 t ha\({}^{-1}\), the model increasingly underestimated yield when the observed values were greater than 6.5 t ha\({}^{-1}\). However, the overall trend in yield with N additions, and differences between years, were very well reproduced.
# Modelling a Spatial-Motion Deep Learning Framework to Classify Dynamic Patterns of Videos
###### Abstract  
Video classification is an essential process for analyzing the pervasive semantic information of video content in computer vision. Traditional hand-crafted features are insufficient when classifying complex video information due to the similarity of visual contents with different illumination conditions. Prior studies of video classifications focused on the relationship between the standalone streams themselves. In this paper, by leveraging the effects of deep learning methodologies, we propose a two-stream neural network concept, named state-exchanging long short-term memory (SE-LSTM). With the model of spatial motion state-exchanging, the SE-LSTM can classify dynamic patterns of videos using appearance and motion features. The SE-LSTM extends the general purpose of LSTM by exchanging the information with previous cell states of both appearance and motion stream. We propose a novel two-stream model Dual-CNNSELSTM utilizing the SE-LSTM concept combined with a Convolutional Neural Network, and use various video datasets to validate the proposed architecture. The experimental results demonstrate that the performance of the proposed two-stream Dual-CNNSELSTM architecture significantly outperforms other datasets, achieving accuracies of 81.62%, 79.87%, and 69.86% with hand gestures, fireworks displays, and HMDB51 datasets, respectively. Furthermore, the overall results signify that the proposed model is most suited to static background dynamic patterns classifications.  
dynamic pattern classification; deep learning; spatiotemporal data; convolution neural network; recurrent neural network +
Footnote †: journal: Applied Sciences
# Wastewater Treatment by Polymeric Microspheres: A Review
## 1 Introduction
Cellulose is economic, non-toxic, and biodegradable natural, renewable polymer which makes it a promising substance for the preparation of bio-based adsorbent. Cellulose beads can be modified into cellulose-based adsorbents. The separation for batch operation is facile, as it can be handled easily in large volume [18]. Carboxylated cellulose nanocrystals (cCNC) can be used to make cellulose microbeads by spray drying from aqueous suspension. The agglomeration of cCNC that are rescaled to micrometer-size particles, which can be used as adsorbents. The cellulose microbeads can overcome some limitations of activated carbon (AC) (which are more expensive) and yield great amount of CO\({}_{2}\) during the wastewater treatment process. The spray drying method is also scalable into industrial processes [19]. Alginate is used widely as adsorbents. Sodium alginate (SA) is an eco-friendly, non-toxic, biocompatible, biodegradable natural polymer. It has abundant functionalities such as carboxyl and hydroxyl groups which can be used beneficially for the removal of heavy metal ions and dyes. One of the limitations of SA is its poor mechanical strength, which is very challenging to improve. The interaction of graphene oxide (GO) and SA is one way to solve the issue [20]. SA can also from hydrogel beads to improve the stability [21]. Introducing other metal ions such as Cu(II) can enhance the adsorption efficiency by increasing the adsorption sites [22]. Chitosan is also a biodegradable and non-toxic abundantly available biopolymer which can be used to adsorb various pollutants, especially the anionic contaminants. A microporous chitin microsphere was made by surfactant micelle swelling method and was used for removal of heavy metal ions [23]. This review addresses polymer-based spheres for wastewater treatment, broadly classified into microspheres prepared from (i) synthetic and (ii) natural polymers, and (iii) polymeric spheres used for photo-Fenton process. The proposed mechanism, influencing factors, stability, recyclability, and efficiency are reviewed to understand the enhanced adsorption removal of dyes, heavy metal ions, and organic pollutants. Among polymeric sphere adsorbents, the photocatalysts are particularly divided for their unique features, and photocatalysts prepared from natural polymers are mainly discussed.
## 3 Results and Discussion
### SnowSense Service for the Island of Newfoundland
#### 3.2.1 Humber River  
The run-off results for Humber Village for the winter season December 2017-May 2018 show very good agreement with the measured run-off reference values (Figure 7). Assimilation of the in situ information, mainly adjusting the solid precipitation, provide a perfect match of the volume of water. For the entire region, the amount of water stored as snow was perfectly represented by the model. The volume of total water released in the winter season 2017/18 was modelled by 95% (80% without assimilation). The coefficient of determination (\(\mathbf{R}^{2}\)) reaches 0.90 at Humber Village. The results for the upper part of the catchment down to Reidville also show a very reasonable accordance between measured and modelled run-off behaviour (Figure 8). Due to invalid run-off measurements during the first flood peak in January 2018, no full analyses of the period were performed. However, the volume of total water released in up to Reidville was modeled by 90% (65% without assimilation). The coefficient of determination (\(\mathbf{R}^{2}\)) reaches 0.80 at Reidville.  
Figure 7: Daily run-off results from PROMET without (grey), with assimilation (blue) of stations and EO against the values (dashed red) measured at Humber Village (catchment: 7600 km\({}^{2}\)) for the winter season 2017/18.
# Compatibility Analysis Between RedCap Non-Public Networks and 5G NR in TDD FR1 and FR2 Bands
## 2 Standardization and Market  
The 3 GPP Partnership Project's Release 17 phase introduced the standardization of RedCap, a technology designed to deliver simpler and more cost-effective Internet of Things(IoT) devices for battery-powered applications. In the June 2019 meetings, working group members presented multiple proposals to address 5G NR requirements and use cases for Internet of Things services, culminating in the introduction of NR-Light technology. This technology, an offshoot of 5G NR, involves reduced-frequency channel bandwidth, fewer MIMO layers, and diminished downlink modulation levels to simplify User Equipment (UE) complexity [5, 6, 7]. Evolving from its initial moniker, NR-Light, the technology is now officially recognized as RedCap and has been incorporated into the 3 GPP Release 17 technical specification series as of June 2022. Figure 1 illustrates the ongoing and upcoming phases of the 3 GPP Partnership Project's work on RedCap technology [8, 9].  
The standardization efforts within Release 18 encompass two phases, with the final phase slated for completion in the second quarter of 2024. RedCap technology, standardized in 3 GPP Release 18, promises to further reduce subscriber terminal complexity, enabling support for a broader range of data rates. This expansion includes the introduction of positioning, sideline protocols, and the utilization of unlicensed spectrum for diverse use cases [10].  
RedCap technology's market trends showcase commercial testing by over 10 mobile operators across seven countries. Projections indicate that RedCap's subscriber device connections in 5G NR IoT networks will surpass 100 million within the next three years, driving the development of novel 5G NR applications.  
China Mobile, China Telecom, and China Unicom, the three largest mobile operators, have implemented extensive RedCap equipment deployments in over 10 cities, spanning industries such as industrial manufacturing, power generation, and V2X intelligent transport networks.  
Anticipated to cost less than USD 10, RedCap subscriber modules in the USA are priced 80% lower than their 5G NR counterparts designed for Enhanced Mobile Broadband (eMBB) services. The MBBF-2023 forum showcased over 10 types of RedCap subscriber modules, including RedCap, DTU, and CPE modules. Qualcomm's release of the Snapdragon X35 5G NR Modem-RF system in 2023 marked an industry milestone as the first 5G NR-Light (RedCap) modem. Supporting up to two antennas and delivering a peak data rate of 220 Mbps with a 20 MHz channel bandwidth in the FR1 sub-band, this modem represents a significant advancement. Devices featuring RedCap technology are poised to offer tenfold more Internet of Things network capacity than 4G technologies, with RedCap devices consuming 20% less energy than comparable 4G counterparts (NB-IoT and LTE-M).
## 7 Detailed D2 WF Protocol
### Acquisition and Preservation  
Acquisition is the process of gathering and collecting evidence, whereas preservation is the act of keeping, protecting, or preventing evidence from deteriorating.  
* Establish a chain of custody: a \"chain of custody\" is a paper trail or chronological documentation that shows the seizure, control, transfer, analysis, and disposition of physical or electronic evidence. When gathering evidence, it is critical to maintain a chain of custody to ensure its integrity and admissibility in court.
* Acquire volatile data following the order of volatility (OOV): Volatile data are information stored in a computer's temporary memory, and these are lost when the system is powered down. It is critical to acquire evidence in the order of volatility to ensure that as much data as possible are recovered from a system. This means that the most volatile data, i.e., data in a system's temporary memory, should be acquired first.
* Acquire physical or logical file system images: a physical file system image is a bit-for-bit copy of a storage device, such as a hard drive, that can be used to forensically examine the device's contents, whereas a logical file system image is a copy of data on a storage device that can be created without creating an exact bit-for-bit copy of the device. Logical file system images typically include only the files and folders required for a specific investigation.
* Import/backup cloud-based content: acquiring and backing up cloud-based content ensures that the evidence is not lost or tampered with if the cloud-based service is shut down or deleted.
* Extract networking and logging data: investigators will often need to collect networking and logging data from reconstructing events or tracking down suspects. This information can be obtained from network devices or servers.  
Figure 5: D2 WFP—deep and dark web forensic protocol branches.  
Secure evidence and store original devices in a safe: once the evidence has been collected, it must be secured and stored in a secure location. This will prevent the evidence from being lost or tampered with. Keeping the original devices in a safe location is also critical to ensure their integrity.
## 3 DASH Interferometer Mount
### Design of Structural Frame  
In contrast to existing technology that uses Invar, the material used in this study is TC4 for the structural frame, consisting of the base, cover, and columns. The heat conductivity of TC4 (7.4 W\(\cdot\)m\({}^{-1}\cdot\)K\({}^{-1}\)) is less than that of Invar (13.9 W\(\cdot\)m\({}^{-1}\cdot\)K\({}^{-1}\)), which can improve the thermal resistance of the DASH interferometer and reduce heat conduction. Meanwhile, the specific stiffness of TC4 is higher than that of Invar; therefore, the natural frequency of the structural frame that satisfies the requirement of rigidity can be improved.
## 5 Efficiency
### Security Property  
Table 1 shows the security properties that each scheme supports. The previous schemes [3,5,6] guarantee data confidentiality in that a passive eavesdropper cannot obtain plain metering data during the aggregation process. Thus, they provide user privacy against curious smart meters or the concentrator. However, they have an inherent weakness against active attacks due to the malleability property of the homomorphic encryption algorithm. Given the ciphertext and public key, an adversary could generate another ciphertext which decrypts to another meaningful plaintext in the same domain as the original plaintext.  
Unlike the previous schemes [3,5,6], the proposed scheme guarantees aggregated data integrity and smart meter authentication through the aggregate signature mechanism, as well as data confidentiality. Thus, the proposed scheme is secure against active attacks such as data manipulation by a dishonest or fake smart meter, or false data injection by outside adversaries, in addition to passive attacks. [PERSON] _et al._'s scheme [21] also guarantees all the security properties, but their scheme allows the verifier to  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Scheme**} & **Confidentiality** & **Data** & **Sender** \\  & **(User Privacy)** & **Integrity** & **Authentication** \\ \hline GJ [3] & yes & no & no \\ LLL [5,6] & yes & no & no \\ LLYLLS [21] & yes & yes & yes \\ Proposed & yes & yes & yes \\ \hline \hline \end{tabular}
\end{table}
Table 1: Security property comparison.  
check integrity of individual metering data, not aggregated one. This incurs much higher communication and computation cost compared to the proposed scheme as will analyze in the next section. A more formal security analysis of the proposed scheme will be given in Section 6.