esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Role of Macrophyte in CW-MFC Contaminant Removal
australis_, _Wachedorfia hyrsiflora_, and the unplanted with 94 \(\pm\) 1%, 94 \(\pm\) 1%, and 90 \(\pm\) 2% removal efficiencies, respectively. The _Coperus profiler_ species attained a higher orthophosphate removal efficiency (98 \(\pm\) 0%) than the control experiment (72 \(\pm\) 7%), _Wachedorfia thyrsiflora_ (58 \(\pm\) 6%), and _Phragmites australis_ (81 \(\pm\) 4%). Comparatively, _Coperus profiler_ was noticed to be the most suitable indigenous wetland plant for electricity production and COD, ammonia, and phosphate removal among the three species [66]. These results also justify [PERSON]'s [62] assertion that different macrophyte species differ in their removal efficiency, hence the need for a proper investigation in selecting macrophyte species to be incorporated into the CWMFC system. Furthermore, studies on plant effects of nitrogen, phosphorus, and heavy metal removal are scanty, compared to the COD removal efficiency. Although generally proven under constructed wetlands (CW), extensive studies are required regarding CWMFC [63]. This study may help optimize the integration of the plant component in CWMFCs and boost the development of CWMFCs for practical use (see Table 1). \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Macophyte**} & **Initial COD** & **COD Removal** & \multirow{2}{*}{**HRT (hr)**} & **Max. Power** & **Author** \\  & **(mg/L)** & **(\%)** & & & \\ \hline _Canna indica_ & 1500 & 74.9 & 96 & 15.7 mW\(\cdot\)m\({}^{-2}\) & [16] \\ \hline _Plragmites australis_ & 1058 & 76.5 & N. A & 9.4 mW\(\cdot\)m\({}^{-2}\) & [67] \\ \hline _Ipomoea aquatica_ & 180 & 86 & 72 & 0.302 W\(\cdot\)m\({}^{-3}\) & [34] \\ \hline _Plragmites australis_ & 250 & 80–100 & N. A & 0.15 mW\(\cdot\)m\({}^{-2}\) & [68] \\ \hline _Ipomoea aquatica_ & 193–205 & 94.8 & 48 & 12.42 mW\(\cdot\)m\({}^{-2}\) & [48] \\ \hline _Ipomoea aquatica_ & 300 & 72.5 & 72 & 0.852 W\(\cdot\)m\({}^{-3}\) & [69] \\ \hline _Plragmites australis_ & 411–854 & 64 & N. A & 0.268 W\(\cdot\)m\({}^{-3}\) & [32] \\ \hline _Typha tafifolia_ & 314.8 & 100 & N. A & 6.12 mW\(\cdot\)m\({}^{-2}\) & [70] \\ \hline _Plragmites australis_ & 583 & 64 & N. A & 0.276 W\(\cdot\)m\({}^{-3}\) & [33] \\ \hline _Taifa tafifolia_ & 624 & 99 & 24 & 93 mW\(\cdot\)m\({}^{-3}\) & [71] \\ \hline _Plragmite australis_ & 323 & 60.6 & 62.4 & 131 mW\(\cdot\)m\({}^{-2}\) & [72] \\ \hline _Elodea nuttalli_ & 643 & 97–98 & 24 & 184.75 mW\(\cdot\)m\({}^{-3}\) & [54] \\ \hline _Canna indica_ & – & 78.71 & 72 & 31.04 mW\(\cdot\)m\({}^{-3}\) & [73] \\ \hline _Plragmites australis_ & 200 & 90.45 & 48 & 0.20 W\(\cdot\)m\({}^{-3}\) & [74] \\ \hline _Plragmites australis_ & – & 82 & 72 & 3714 mW\(\cdot\)m\({}^{-2}\) & [75] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: COD removal efficiency and power density between different macrophytes based on earlier studies.
## Abbreviations  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l}
5G & Fifth-Generation Mobile Technology \\
6G & Sixth-Generation Mobile Technology \\ AF & Application Function \\ AMF & Access and Mobility Management Function \\ B5G & Beyond 5G Technologies \\ BWP & Bandwidth Part \\ CN & Core Network \\ CPU & Central Processing Unit \\ CU & Centralized Unit \\ DL & Deep Learning \\ DQN & Deep Q-Network \\ DRL & Deep Reinforcement Learning \\ DU & Distributed Unit \\ eMBB & Enhanced Mobile Broadband \\ FDRL & Federated Deep Reinforcement Learning \\ FL & Federated Learning \\ gNB & Next Generation Node B \\ HFL & Horizontal Federated Learning \\ ILP & Integer Linear Programming \\ MCS & Modulation and Coding Scheme \\ MEC & Multi-Access Edge Computing \\ ML & Machine Learning \\ mMTC & Massive Machine-Type Communications \\ near-RT RIC & Near-real-time RAN Intelligent Controller \\ NEF & Network Exposure Function \\ NF & Network Function \\ O-CU & Open Centralized Unit \\ O-DU & Open Distributed Unit \\ OFDM & Orthogonal Frequency Division Multi-plexing \\ O-RAN & Open Radio Access Network \\ O-RU & Open Radio Unit \\ PRB & Physical Resource Block \\ QoS & Quality of Service \\ RAM & Random Access Memory \\ RAN & Radio Access Network \\ rApps & Non-RT RIC Applications \\ RB & Resource Block \\ RF & Radio Frequency \\ RIC & RAN Intelligent Controller \\ RL & Reinforcement Learning \\ RU & Radio Unit \\ \end{tabular}
# A New Methodology for Type Synthesis of Planar Linkages for Exoskeletons up to Five Angular Outputs
## 2 Design Basics  
In a simple mechanism design, linkage can be defined as a system of rigid elements or links that are connected to transform forces or motion. The desired output is obtained by a single end-effector which is attached to the output link. Usually, many kinematic chains may achieve the required output. Therefore, the design problem is to select the optimum mechanism to realize the output motion. If two or more output links are required, there are numerous candidates of planar topologies that may have two or more output links.  
Generally, exoskeleton design procedures, which target rehabilitation processes, depend on some important factors such as:  
1. Type of human limb (lower limb, upper limb, or finger);
2. Type of required motion;
3. Number of human joints that are required to be rehabilitated and their trajectory;
4. The available workspace for exoskeleton fixation;
5. The ability of patient to carry the proposed mechanism.  
Angular output can be defined as the angular motion obtained by the output link in the considered mechanism. However, one of the most important design aspects is the number of required angular outputs that should be achieved by the exoskeleton. A one DOF rehabilitation robot, with a specific number of angular outputs, is preferrable. Therefore, a single actuator will be required. It may be connected to the base of the exoskeleton to reduce the apparent inertia of the mechanism. It is aimed to introduce a complete set of one DOF planar topologies that are classified according to the number of angular outputs. It is noticed that a certain mechanism with \(m\)-angular outputs can be used for an exoskeleton application with up to \(m\)-angular outputs.  
For the type synthesis process, a four-bar mechanism is used as an initial linkage that has one angular output motion. Only revolute joints are used to simplify assembly and control of the considered exoskeleton. Revolute joints also are preferable for planar mechanisms. A minimum number of links and joints are required for the proposed exoskeleton design to ensure compactness and light weight.
## 3 Discussion
### _RrCaCA_ and _RrCAX1a_ Mechanisms in R. roxburghii  
Calcium transport in plants involves critical genes such as P2A- or P2B-type Ca\({}^{2+}\)-ATPases (ACAs) and CAXs, which actively remove excess Ca\({}^{2+}\) from the cytoplasm to maintain ion homeostasis [35; 36; 37]. The knockout of _CAX1_ has been shown to reduce cell wall extensibility [38]. Under NH\({}_{4}\)+ stress, calcium deficiency in tissues disrupts Ca\({}^{2+}\) transport and localization in _Arabidopsis_ leaves. The overexpression of _CAX1_, especially a modified version lacking its autoinhibitory domain, reduces apoplastic Ca\({}^{2+}\) deficiency and alleviates NH\({}_{4}\)+-induced growth stunting by restoring Ca\({}^{2+}\) homeostasis [39]. These findings suggest that _RrCAX1a_, identified in this study, likely plays a similar role in calcium transport and stress adaptation in _R. roxburghii_.  
_CAX1_ is the most highly expressed and differentially regulated transporter of Ca\({}^{2+}\) between epidermal and mesophyll cells. The vacuolar localization of _CAX1_ in Ca\({}^{2+}\) transport was demonstrated through heterologous expression in yeast and other plant species [38]. Our functional validation of _RrCAX1a_ supports this hypothesis. Subcellular localization analysis confirmed its plasma membrane localization. The overexpression of _RrCAX1a_ in tobacco significantly enhanced calcium stress tolerance, as evidenced by reduced wilting and improved growth under high-calcium conditions (Figure 8). These results align with observations in tomato seedlings. The expression of _CAX1_ correlates positively with Ca\({}^{2+}\) accumulation, with the highest expression levels observed in leaves, whereas _CAX3_ is more abundantly expressed in roots. In tomato seedlings treated with various ions, only _SICAX1_ and _SICAX3_ were significantly induced by the Ca\({}^{2+}\) treatment, suggesting their roles in Ca\({}^{2+}\) transport [40]. Similarly, _RrCAX1a_ likely plays a pivotal role in _R. roxburghii_ adaptation to high-calcium environments, contributing to its remarkable tolerance by regulating calcium homeostasis and mitigating calcium-induced stress.
## 1 Introduction
Some studies have reported that the observations of midlatitude plasma depletion were associated with MSTIDs in the absence of EPBs signatures in the equatorial region during three different magnetic storms [4; 16; 30]. In addition to these two common mechanisms, it is also found that some driving factors can locally produce midlatitude plasma depletion, especially in the dayside ionosphere, when ionospheric F-region irregularities usually do not occur. [PERSON] et al. [32] investigated a dayside midlatitude plasma depletion without fine structures at altitudes near 430-520 km during quiet geomagnetic conditions, excluded the possibility of an equatorial depletion source, and believed that it may be related to gravity waves. In addition, unusual dayside midlatitude plasma depletions were detected a few hours after rocket launches, due to chemical recombination caused by exhaust plumes along the rocket's ascending trajectories [33]. Overall, the possible generation mechanisms of midlatitude plasma depletions are diverse and still not well understood, which is worthy of further investigation with more observations and events. In this study, we present an intriguing case study of midlatitude plasma irregularities over Eastern Asia that occurred during a moderate magnetic storm on 16 July 2003. By jointly using multi-instrumental observations, we investigated the roles of EPBs and MSTIDs in generating midlatitude plasma depletion. The results show the day-to-day variability of the occurrence and characteristics of ionospheric F-region irregularities. A detailed description of the data is included in Section 2, while the analysis results are presented in Section 3. Our discussions and conclusions are contained in Sections 4 and 5, respectively.
## 3 Visualization and Clustering  
At first, a text's dataset has to be parsed, all textual information is extracted from the original source and Meta information is not included (pictures, tables frames, schemes, and other not necessary information are rejected). After parsing is done, tokenization has to be made. Tokenization is a process of breaking a stream of text up into words, phrases, symbols, sentences or other meaningful elements  
Figure 1: The scheme of the proposed approach; SOM: self-organizing maps. Figure 2: The process of creation of frequency matrix. called tokens. The list of tokens becomes input for further processing, such as text mining. Afterwards, we can choose a different filter. It is obvious that all texts have some information that do not characterize the text or is simply not important in the text analysis. Therefore, the aim of selected filters is to reject not important information from texts datasets, such as numbers, punctuation, stop words, etc. The most popular filters and their descriptions are presented in Table 1. In some text mining systems, a specific filter which helps to reject just links, keywords or other not ordinary information can be found. It is obvious that the most important part in text conversation is filter selection because it has the biggest influence on the results. So, it is important to choose the right options, to get accurate results [15]. Otherwise, useful information can be rejected and results can be inappropriate. According to the selected filters, a so-called bag of words is created. The bag of words is a list of terms from texts excluding the words that do not satisfy the conditions defined by the selected filters. Suppose we have a texts dataset \(D=\{D_{1},D_{2},\ldots,D_{N}\}\). According to the frequency of the words in the texts, a so-called frequency matrix is created:  
\[\left( \begin{array}{cccc}x_{11}&x_{12}&\ldots&x_{1m}\\ x_{21}&x_{22}&\ldots&x_{2m}\\ \vdots&\vdots&\ddots&\vdots\\ x_{N1}&x_{N2}&\ldots&x_{Nm}\end{array} \right) \tag{1}\]  
Here \(x_{pl}\) is the frequency of the \(l\)th word in the \(p\)th text, \(p=1,\ldots,N\), \(l=1,\ldots,m\). \(N\) is the number of the analyzed texts, and \(n\) is the number of words in the bag of words. In the simplest case, frequency value is equal to number that shows how many words appear in the text. A row of matrix (1) is a vector, corresponding to a text. The vectors \(X_{1},X_{2},\ldots,X_{N}\), \(X_{p}=\left(x_{p1},x_{p2},\ldots,x_{pm}\right)\), \(p=1,\ldots,N\), can be used for a text analysis using various methods. Sometimes it is not enough to analyze just words extracted from texts, especially when similarity has to be found. The analysis of n-grams can be used [17]. An n-gram is a contiguous sequence of \(n\) items from a given sequence. The item can be described as word, letter, phonemes, etc. In our research, we have used a word as the item. In this way, we have a bag of n-grams, where each text is characterized by unique n-grams (a few words from the texts). The n-grams analysis allows to
# Establishing a Generic Geographic Information Collection Platform for Heterogeneous Data
## 1 Introduction
However, most mobile collection platforms focus solely on data collection and miss key features like background management systems, making them incapable of supporting workflows involving task allocation and heterogeneous data visualization [28; 34]. Researchers must integrate ArcGIS Field Map or QField with commercial online storage services and desktop software to manage data collection, upload, storage, and visualization [28; 39; 40; 41; 42]. This process requires specialized knowledge, is not user-friendly, and supports only online data storage, not local servers [28]. Although ODK offers local server deployment, it requires a domain name and only supports PostgreSQL for data storage [33; 43]. Importantly, these combined platforms still lack functions for task assignment and monitoring collection progress [44]. These problems may lead to erroneous data collection and waste of effort in assigning tasks, especially in large-scale data collection tasks where many task forces are distributed over a wide area, making communication extremely inconvenient [39]. Table 1 intuitively shows the detailed functions supported by the currently popular generic collection platforms. In summary, the current generic mobile collection platforms still need more versatility and only focus on field data collection, ignoring other equally important steps in field geographic information collection work. Therefore, it is necessary to continue to develop a generic geographic information collection platform with greater versatility and support for the entire field geographic information collection process. To bridge the gap, this paper proposes a generic geographic information collection platform. The platform comprises collection software supporting multiple runtime platforms such as iOS, Android, and the web, as well as a management system compatible with various databases such as PostgreSQL, MySQL, and Oracle. Our platform adopts a comprehensive XML schema definition to address heterogeneous data collection problems and designs an XML toolkit capable of parsing configuration files, creating database tables, interacting with databases, and rendering forms in collection software. An innovative aspect of this platform is its diverse geographic information data collection workflow and rich set of configurable controls, thereby supporting complex data collection scenarios. Moreover, our platform supports multitasking parallelism, allowing users to handle multiple tasks simultaneously. In order to improve the adaptability of the platform, our platform also offers cross-platform compatibility, cross-database compatibility, and the ability to process data collection tasks offline or online.
## 3 The Modeling and Simulation Architecture
### Ptolemy II Modeling Tool: Building WSAN Models  
We used Visual Sense for modeling and simulating the WSAN. Visual Sense is a framework for building models that include sophisticated elements from various aspects and is built on Ptolemy II. Visual Sense provides facilities for modeling communication channels, sensor channels, ad-hoc network protocols, media access control protocols, power consumption at sensor nodes, etc. [19; 20].  
A characteristic to highlight in the elaboration of a WSAN model is the use of \"Accessors\" of Ptolemy II [21]. Accessors extend the applicability of Ptolemy to modeling IoT systems, allowing models to communicate with the computer's input and output interfaces. Our model uses the accessors for communication with an MQTT Message Broker: accessor-net/MQTT-Publisher and accessor-net/MQTT-Subscriber.
# Dual-Mic Speech Enhancement Based on TF-GSC with Leakage Suppression and Signal Recovery
## 5 Conclusions  
In this paper, we introduced two additional modules to mitigate the effect of the steering error of the TF-GSC with postfiltering. The leakage suppression module suppresses the leaked desired signal in the output of the BM by applying a spectral gain similar to the square-root Wiener filter. On the contrary, the signal recovery module restores the attenuated desired signal in the FBF output by adding a certain amount of the appropriate microphone signal, which is chosen by examining the cosine similarities between the microphone signals and the outputs of the FBF and BM. The experimental results showed that the two proposed modules improved the performance of the conventional TF-GSC  
Figure 8: Average PESQ scores and 95% confidence intervals for the outputs of the baseline and proposed beamformers depending on the azimuths of the desired source and a competing talker with an RT60 of 500 ms.  
Figure 7: Average PESQ scores and 95% confidence intervals for the outputs of the baseline and proposed beamformers depending on the azimuths of the desired source and a competing talker with an RT60 of 300 ms.  
with postfiltering, both in diffuse noise environments and in the presence of a competing talker, achieving almost the same PESQ scores for all of the DoAs of the desired signal.  
Conceptualization, H.K. and J.W.S.; methodology, J.W.S.; software, H.K.; validation, J.W.S.; formal analysis, J.W.S.; investigation, H.K.; resources, J.W.S.; data curation, H.K.; writing--original draft preparation, H.K.; writing--review and editing, J.W.S.; visualization, H.K.; supervision, J.W.S.; project administration, J.W.S.; funding acquisition, J.W.S. All authors read and agreed to the published version of the manuscript.  
This research was funded by the Ministry of Culture, Sports, and Tourism (MCST) and the Korea Creative Content Agency (KOCCA) in the Culture Technology (CT) Research and Development Program (project number: R2019080018) and Samsung System LSI (SLSI-201801 GE002S).  
The authors declare no conflict of interest.
# AI-Based Analysis of Policies and Images for Privacy-Conscious Content Sharing
## 4 Image Privacy Recommendations
### Numerical Feature Extraction
#### 4.1.1 Hue-Saturation Data  
A digital image can be, at a lower level, represented by a matrix of points: each point holds one or more values that can be represented as a tuple. Those tuples belong to a given **color space**, such as _RGB_ (red-green-blue), _CMYK_ (cyan-magenta-yellow-black), etc. For extracting these features, we use the _HSV_ (hue-saturation value) color space, as it separates the color from its intensity (the value). For each image, we extract the hue and saturation histograms, which are divided into 16 bins each. The values inside the bins are normalized using the number of pixels of the image to prevent values growing proportionally to the image size.
## 3 Preliminaries
### Program Execution Monitoring Based on Basic Block
#### 3.1.3 Integrity Signature Calculation  
As described above, the extraction of \(INF_{inf}\) from each BB requires the LHash algorithm to calculate the BB digest signature for integrity monitoring. This light-weight cryptographic hash algorithm was first reported in the study [27], where an internal permutation employed the kind of Feistel-PG extended sponge structure for utilizing the permutation layers on nibbles to improve the diffusion speed. This was a hardware-friendly hash function (only 817 gate elements) and was suitable to be implemented in embedded systems to quickly transform a given sequence of instructions into a fixed bit-number of integrity signature.  
As shown in Figure 2, the LHash algorithm was utilized to calculate the instruction information and generate the 16-bit integrity signatures for BB1 to BB4, respectively. Furthermore, an important consideration of applying the LHash algorithm is utilizing its sequential iteration mechanism in the sponge structure to recalculate the integrity signature of a sequence of instructions during program execution, which is then compared with the value of the previously-stored LHash-16 integrity digest in the \(INF_{inf}\) table. Consequently, this algorithm will quickly complete the integrity signature calculation under a low hardware complexity.
## 4 Experiments
### Ablation Experiments and Result Analysis
#### 4.4.6. EMF + CASAM  
The EMF and CASAM adopt a parallel architecture design in the network, achieving collaborative extraction of multi-scale features through functional complementarity. The ablation experiment data in Table 1 show that removing either module alone only causes partial degradation of model performance, while removing both modules simultaneously makes the model completely lose the ability to extract multi-scale features. The F1 and IoU metrics on the three datasets all show significant degradation, with F1-scores decreasing by 2.67%, 3.32%, and 7.3%, respectively, and the corresponding decreases in IoU metrics reaching 3.31%, 4.85%, and 6.31%, verifying the complementarity of the two in multi-scale feature modeling. The visualization results in Column c of Figure 6. show that the model missing the EMF + CASAM misses the detection of multiple small building changes in the LEVIR-CD scene, with a significant increase in green areas, confirming the key role of cross-scale feature fusion in small-target detection. In the WHU-CD scene, due to the similarity between the land color in period A and the roof color in period B, the limitation of single-scale representation makes it difficult to distinguish the target area due to color confusion, resulting in missed detection.  
Further analysis shows that the collaborative absence of the two modules causes the model to fail in integrating multi-level semantic information and cross-scale structural features--it can neither capture the fine features of small targets through the pyramid structure nor use cross-layer interaction to enhance the structural coherence of large-scale regions, especially causing significant missed detections in small-target detection scenarios. This phenomenon reveals that the adaptive representation of multi-scale features for target size differences is crucial. In the absence of multi-path feature interaction, the model's ability to model pixel-level details and contextual dependencies is simultaneously weakened, ultimately leading to a comprehensive decline in detection performance. The experimental results fully verify the necessity of the EMF + CASAM in the change detection network proposed in this paper.