esa-sceva/satcom-chunk-collection huggingface dataset:
# FL-LoRaMAC: A Novel Framework for Enabling On-Device Learning for LoRa-Based IoT Applications
###### Abstract
We introduce a novel SF allocation scheme that was presented in our preliminary work in [13], which, in association with traditional optimization tools such as principal component analysis (PCA) and pruning of the AI model, significantly reduces the communication requirements in the network. The communication requirements of FL using the proposed framework for its implementation in a representative healthcare application using LoRa-based networks are examined numerically. We present the design considerations and performance evaluations of FL-LoRaMAC by considering the problem of the anomaly detection of ECG signals as a candidate IoT-based  
Figure 1: LoRaWAN architecture. AI application. This is motivated by the fact that, currently, ECG signals may be obtained from low-cost portable devices such as smartwatches that utilize photoplethysmography (PPG) technology [14]. The easy availability of ECG data from FDA-approved sensors integrated into smartwatches by manufacturers such as Apple, Google Pixel, Fitbit, and Samsung has paved the way for AI applications. A trained model with ECG datasets can have the ability to detect or predict heart conditions. However, the model must be able to differentiate between healthy (normal) and unhealthy (abnormal) ECG signals to detect health conditions effectively. It must be noted that the proposed approaches would be useful for a large number of other applications of AI in IoT networks as well. To summarize, the major contributions presented in this paper are:  
* An evaluation and comparison of the performance of ML and FL using a representative healthcare application. * The development of a novel framework used to implement FL for LoRa-based IoT platforms. The framework includes an MAC protocol that enables two-way half-duplex communication between the central server and the end devices while optimizing power consumption. * The introduction of a novel SF allocation scheme that assigns SFs for uplink data transmissions to provide differential priorities of the parameters being transmitted. * An evaluation of the performance of the proposed AI scheme, including accuracy, precision, and recall under channel packet loss rate, PCA sparsification, and number of devices. The organization of the paper is as follows. In Section 2, we summarize related work. The problem statement and design considerations are presented in Section 3. The proposed framework is described in Section 4. The simulation setup for performance evaluations is described in Section 5 and results are presented in Section 6. Section 7 includes conclusions and future work.
## 3 Materials and Methods
### Index Weight Calculation  
The method of combining subjective and objective indices was used to calculate the index weight, whereby the analytic hierarchy process (AHP) was used to calculate the subjective weight, and the entropy method was used to calculate the objective weight. Firstly, the subjective weight and objective weight of each level index were calculated according to the level of index division. Then, the subjective weight and objective weight were combined to obtain the comprehensive weight. Lastly, the scores of each index were calculated according to the comprehensive weight. (1) Subjective weight calculation using AHP  
(a) Constructing the first-order index judgment matrix  
The discrimination matrix is established by pairwise comparison of the ratio of the importance of factor \(i\) to factor \(j\). (b) Consistency test  
\[CI=\frac{\lambda_{max}-n}{n-1}. \tag{6}\]  
\[CR=\frac{CI}{RI}. \tag{7}\]  
(c) Calculating the weight proportion of each level of indicators  
Construction of judgment matrix:  
\[C_{j}=\frac{1}{n}\sum_{j=1}^{n}\frac{a_{ij}}{\sum_{k=1}^{n}a_{kj}};(i=1,2, \cdots,n). \tag{8}\]  
(2) Objective weight calculation by entropy method  
(a) Calculation of standardized matrix  
The range standardization method was used for processing, and the original data were transformed into a unified dimensional value by linear transformation. The calculation method was as follows:  
\[b_{ij}=\frac{x_{ij}-min\big{(}x_{ij}\big{)}}{max\big{(}x_{ij}\big{)}-min\big{(} x_{ij}\big{)}}. \tag{9}\]  
(b) Calculation of probability matrix \(P\)  
\[p_{ij}=\frac{b_{ij}}{\sum_{i=1}^{m}b_{ij}}. \tag{10}\]  
The sum of the probabilities of each index was guaranteed to be 1. (c) Calculation of information entropy  
\[h_{j}=-(\ln n)^{-1}\sum_{i=1}^{m}p_{ij}\ln p_{ij}. \tag{11}\]  
When \(p_{ij}=0\) and the \(p_{ij}\ln p_{ij}=0\), then \(0\leq h_{j}\leq 1\). 4. Calculation of the information utility value  
Greater information entropy indicates less information, which needs to be converted into information utility value. A greater information utility value corresponds to more information. The calculation formula of information utility value is as follows:  
\[d_{j}=1-h_{j}. \tag{12}\]
5. Calculation of the entropy weight of each index  
The entropy weight of each index (\(W_{j}\)) is obtained as follows:  
\[W_{j}=\frac{d_{j}}{\sum_{j=1}^{n}d_{j}}. \tag{13}\]  
(3) Comprehensive weight of indicators  
According to the first two steps, the weights under subjective and objective weighting methods were obtained, and the following formula was used to calculate the subjective and objective comprehensive weights:  
\[K_{j}=\frac{\sqrt{C_{j}W_{j}}}{\sum_{j=1}^{n}\sqrt{C_{j}W_{j}}}. \tag{14}\]  
(4) Comprehensive score of indicators  
\[S=\sum_{j=1}^{n}K_{j}\times score_{j}, \tag{15}\]  
where \(n\) is the number of composite indicators, from which the scores of economic comprehensive index and ecological comprehensive index can be obtained. The results are shown in \[CR=\frac{CI}{RI}. \tag{7}\]0
# A Lightweight Reconstruction Model via a Neural Network for a Video Super-Resolution Model
## 2 Materials and Methods
### Recursive Residual Network (RRN)  
The RRN [29], a significant model within deep learning, offers a powerful mechanism for reconstructing super-resolution images. By leveraging the unique architecture of recursive and residual connections, the RRN model proficiently enhances the detail and lucidity of low-resolution images through several iterative processes and progressive layer enhancement.  
Figure 1: Schematic illustration of three commonly used temporal modeling frameworks: (**a**) the RNN, (**b**) the DRRN, and (**c**) the DBRRN.  
The RRN model's quintessential philosophy revolves around super-resolution reconstruction, achieved by recursively learning and iteratively updating residual information. The structure of the model incorporates numerous stacked recursive units that consist of a CNN and a residual learning module. Within each recursive unit, the CNN focuses on feature extraction and upscaling low-resolution images, whereas the residual learning module involves acquiring and transmitting residual data. The model incrementally augments the image resolution and quality through the repeated accumulation of these units.  
Conventionally, the training of the RRN model adopts an end-to-end approach, creating a direct correlation from low-resolution images to their high-resolution equivalents. The model's parameters are optimized and revised using a pixel-level loss function (like mean square error), which gauges the deviation between the super-resolution image produced and the original high-resolution image. A perceptual loss function, such as those predicated on perceptual distance (including perceptual loss and content loss), can be utilized to boost the visual fidelity of the reconstructed images.  
The RRN model has accomplished substantial breakthroughs within the image super-resolution domain, substantiating the model's aptitude to enhance image detail restoration and visual quality through recursive learning and residual connections. While maintaining computational efficiency, it delivers high-quality super-resolution reconstruction. Additionally, the RRN model has the potential to be collaboratively fused with other technologies and models, like attention mechanisms and Generative Adversarial Networks (GANs), thereby further escalating the super-resolution outcome.  
In conclusion, the RRN model is an efficacious deep learning model for image super-resolution reconstruction tasks. Through its recursive and residual connection structure, the model progressively enhances the detail and clarity of images, offering essential technical support for image processing and application endeavors.
# Efficient Key-Value Data Placement for ZNS SSD
## 2 Background
### RocksDB  
Facebook developed RocksDB, a log-structured merge-tree (LSM-Tree)-based open-source key-value store. Unlike other key-value stores, it enables the use of multi-threading in the flush and compaction routine [20].  
An LSM-Tree reduces random writes using out-of-place updates and has a compaction routine to collect the invalid data generated by the out-of-place update. They cause the LSM-Tree to have a fast write performance and help to utilize the space efficiently. These are the main reasons why the LSM-Tree is a backend of the key-value store and the main reasons why the LSM-Tree is adopted by various key-value store backends [4; 20].  
Figure 2 shows the RocksDB architecture that consists of the Memtable, write-ahead log (WAL) file and sorted sequence table (SSTable) file. The Memtable is a write buffer existing in the memory which aggregates the user write I/O until the buffer threshold is reached. Buffering creates coarse-grained write I/O and it permits to utilize the disk more. SSTable is generated by compaction results. It is the centerpiece of the RocksDB with LSM-Tree. RocksDB compaction uses leveled compaction. Leveled compaction restricts the number of overlapped tables to achieve optimal read performance. However, it struggles with high write amplification problems when it executes the sorted merge [6]. Therefore, the SSTable located in Level 1 has a problem due to write amplification.  
Figure 1: Data placement on the (**a**) conventional Solid-state Drive (SSD) and (**b**) zoned namespace SSD.  
Figure 2: RocksDB architecture with ZenFS. L\({}_{n}\) represents level n of LSM-tree in RocksDB. Zone #0–#7 represent a part of zones of ZNS SSD.
## 4 Evaluation
### User Experience  
After interacting with MIRA, the participants were asked to fill out the User Experience Questionnaire (UEQ) [59], which covers all the aspect of user experience in a comprehensive way. The UEQ is widely used as a subjective measurement of user experience and provides a data analysis tool for assessing user responses. Therefore, we used it to evaluate the MIRA user experience. It consists of 26 items using a 7 point Likert scale for rating. The results of these 26 items are mapped with 6-dimensional scales such as attractiveness (6 items), perspicuity (4 items), efficiency (4 items), dependability (4 items), stimulation (4 items), and novelty (4 items) as shown in Figure 8. The \(x\)-axis and \(y\)-axis present the list of items and rating scales (extremely good(+3), neutral (0), horribly bad (\(-\)3)),respectively. Furthermore, the 6-dimensional scales are grouped into pragmatic quality (perspicuity, efficiency, and dependability), and hedonic quality (stimulation, originality). The pragmatic deals with task-related quality aspects, while the hedonic describes non-task related quality aspects.  
Figure 9 illustrates the result of MIRA based on 6-dimensional scales, which exhibit accurate measurements because the values are greater than 1.6. Moreover, Figure 10 presents MIRA's attractiveness and pragmatic quality along with hedonic quality, where the value is greater than 1.80, reflecting a positive evaluation based on UEQ criteria. To identify the correlation of items per scale, UEQ uses Cronbach's alpha-coefficient, which measures the consistency of a scale as shown in Table 5. The value of attractiveness is higher than 0.7, which means that all users enjoyed the interactions with MIRA. Most of the participants recommend an avatar instead of a simple user interface for MIRA. Therefore, the alpha-coefficient value of novelty was less than 0.5. Furthermore, Figure 11 presents a comparative analysis of MIRA based on the UEQ benchmark dataset, which consists of 401 product evaluations collected from 18483 participants. The results show that MIRA is relatively good in all aspects based on the benchmark data.  
Figure 8: MIRA user experience questionnaire mean value per item.  
Figure 10: MIRA user experience questionnaire aggregated score of pragmatic and hedonic qualities.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Scale** & **Alpha-Coefficient** \\ \hline Attractiveness & 0.74 \\ Perspective & 0.67 \\ Efficiency & 0.77 \\ Dependability & 0.60 \\ Stimulation & 0.67 \\ Novelty & 0.48 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Correlation of items per scale using [PERSON]’s Alpha Coefficient.  
Figure 9: MIRA user experience questionnaire resulting scores on six dimensional scale.
# Conversion of Stearic Acid into Bio-Gasoline over Pd/ZSM-5 Catalysts with Enhanced Accessibility
## 2 Experimental Procedure  
### Catalysts Preparation  
The hierarchical ZSM-5 supports were synthesized by a protozoelitic units silanization method published in Reference [35], using the following reagents: tetraethylortosilicate (TEOS, 98%, Sigma-Aldrich, St. Louis, LA, USA), tetrapropylammonium hydroxide (TPAOH, Alfa, 40% v/v aqueous solution), aluminum isopropoxide (AIP, Sigma-Aldrich, St. Louis, LA, USA), phenylaminopropyltrimethoxysilane (Sigma-Aldrich, St. Louis, LA, USA), and distilled water. Initially, TPAOH and AIP were dissolved in distilled water and cooled in an ice bath under stirring for several hours until a clear solution was observed. Then, TEOS--previously cooled in ice--was poured dropwise into the solution and left mixing for 1 h. The composition of these mixtures was x Al\({}_{2}\)O\({}_{3}\):60 SiO\({}_{2}\):11 TPAOH:1500 H\({}_{2}\)O, where x was varied for obtaining Si/Al molar ratios in the gel of 30, 50, and 100, respectively. The solutions were stirred for 40 h at ambient temperature until TEOS was completely hydrolyzed and then the formed ethanol was evaporated in a rotary evaporator at 60 \({}^{\circ}\)C. The obtained gels were pre-crystallized at 90 \({}^{\circ}\)C for 20 h under reflux to form protozeolitic units. Crystallization was then interrupted by adding 8 mol%, (on a silicon basis) of the silanization agent, keeping the reflux at 90 \({}^{\circ}\)C for 6 h. The suspension of silanized units was finally crystallized at 170 \({}^{\circ}\)C in an autoclave reactor for 7 days. The obtained solids were removed from the synthesis medium by centrifugation and rinsed with distilled water. Subsequently, they were dried at 110 \({}^{\circ}\)C overnight and calcined in static air at 550 \({}^{\circ}\)C for 5 h. The materials were called as h-ZSM-5 (X), wherein X stands for their actual Si/Al molar ratio.  
Palladium was added on the supports by means of impregnation with a PdCl\({}_{2}\) (99.999%, Aldrich) aqueous solution. The Pd concentration in the solution was adjusted to attain 1 wt % metallic Pd on the final catalysts, employing a ratio (pore volume)/(solution volume ratio) of 1/2. After impregnation, the samples were put in an ultrasonic bath for 30 min and subsequently in a rotary evaporator under vacuum at ambient temperature for 5 h. Finally, they were dried at 110 \({}^{\circ}\)C for 12 h and then calcined in a muffle oven at 550 \({}^{\circ}\)C for 5 h under static air. Calcined samples were subjected to reduction under 30 mL min\({}^{-1}\) of H\({}_{2}\), the temperature increased to 450 \({}^{\circ}\)C with a ramp rate of 2 \({}^{\circ}\)C min\({}^{-1}\) and held for 30 min. Subsequently, the samples were cooled down to ambient temperature under N\({}_{2}\) flow. Then, they were stored in a closed vessel under room conditions until being used in reaction.  
In order to make proper comparisons, a conventional nanocrystalline ZSM-5 zeolite (Sudchemie), named as n-ZSM-5 (32), was also impregnated, characterized, and tested.
## 4 Results
Each block may contain some of the next components: look-up table (LUT), flip-flop (FF), block RAM (BRAM ), input/output pins (IO) and buffers (BUFG). Regarding Figure 22, the current transmitter design requires 21 IO lines which account for the physical layer frame output encoded by a fixed-point format employing 14 for the word length and also several control signals from the AXI-stream interfaces. Apart from this, the total use of digital components is below 1% of the total number of available components. Thus, it is safe to state that the current design is resource efficient, which is desirable since additional resources are available to allocate extra applications within the system on chip. Lastly but no less significantly, the design allows system redundancy which is crucial since backup systems are needed to fulfill the requirements of modern space communications systems in case of failure.
# Quasi-Isotropic Hybrid Dielectric Resonator Antenna--Bow-Tie Patch with Harmonic Suppression
###### Abstract  
This paper proposes a quasi-isotropic hybrid dielectric resonator antenna (DRA) and bow-tie patch with harmonics suppression. The suggested antenna consists of a DRA, a bow-tie patch, and a microstrip seventh-order Chebyshev low-pass filter. By loading a bow-tie patch on the designed DRA, a quasi-isotropic pattern is realized. The seventh-order Chebyshev low-pass filter was applied to the feed line, and harmonics were reduced in the section separate to the operating band to remove harmonics generated by the proposed antenna. The simulated S11 that satisfies below \(-10\) dB is 3.09-3.3 GHz (6.25%), and the measured S11 is 3.10-3.28 GHz (5.64%). The simulated gain difference considering all radiating regions (\(0^{\circ}\leq\varphi\leq 360^{\circ}\) and \(0^{\circ}\leq\theta\leq 180^{\circ}\)) is 7.211 dB. Compared with the antenna without a filter, the harmonic gain was reduced by 10.847 and 15.774 dB. The measured gain isolation of the operating band and the second and third harmonics are 10.10 and 18.94 dB, respectively. The proposed antenna is considered to be applicable to applications that require radio wave reception in all directions such as wireless point access points, internet of things and radio frequency identification, and is expected to contribute to reducing the size of the RF system.  
quasi-isotropic pattern; harmonics suppression; filtering antenna; dielectric resonator antenna; bow-tie antenna +
Footnote †: journal:  
_Applied_  
_sciences_
## 4 Computational Results and Discussion
### Flow Characteristics  
The velocity profile under the stimulus of Weissenberg number \(W_{e}\), power law index/fluid index \(n_{i}\), electroosmotic parameter \(m_{e}\), Helmholtz-Smoluchowski velocity \(U_{HS}\) and volume flow rate \(\theta\) is shown in Figure 2a-e. Figure 2a,b illustrates the variation of axial velocity for increasing values of \(W_{e}\) and \(n_{i}\). It is predicted that the magnitude of \(u\) increases as \(W_{e}\) increases and decreases as \(n_{i}\) increases. But opposite behavior is witnessed near the channel walls. Because, \(W_{e}\) is directly proportional to the time constant and inversely proportional to the width of the channel. Figure 2c demonstrates that electroosmotic parameter \(m_{e}\) enhances the magnitude of \(u\) i.e., magnitude of \(u\) increases as \(m_{e}\) increases at centerline of the channel. Figure 2d reveals that \(u\) decreases for higher values of \(U_{HS}\) in the center of channel, whereas opposite behavior is noticed near walls. Since, \(U_{HS}\) physically interprets that fluid velocity reduces with greater EDL thickness. Figure 2e displays the impact of u with respect to \(\theta\). It has also been noticed that change in \(\theta\) increases the \(u\) because flow rate is directly proportional to the fluid velocity. We observed that our study is consistent with previous results [26].  
Figure 2: \(Cont.\)
## 2 Latest Developed Biosensors for COVID-19
### Field Effect Transistor (FET) Biosensor  
As shown in Figure 7A, a typical FET-based biosensor consists of a semiconductor substrate with three terminals: (1) the source, (2) the drain and (3) reference or gate in contact with an electrolyte. The source and drain terminals are attached to the semiconducting substrate and a thin oxide layer (insulator) is deposited between these two terminals. Generally, biorecognition elements such as antibodies are immobilised on the oxide layer (sensor surface) to complete the biosensor construction. When a bias voltage is applied across the gate, an electric field is generated and charge carriers flow in the semiconductor channel from the source electrode to the drain electrode. The direction of current depends on the type of channel, either \(p\)-type or \(n\)-type [95]. The target molecules (e.g., protein, nucleic acid) usually carry charges and they will affect the current when they are bound to the recognition elements immobilised on the sensor surface. The current can thus be monitored as a function of time to detect the target molecules. For the \(p\)-type channel, negatively charged target molecules captured by the biorecognition elements accumulate positive charge carriers (holes) in the channel, which increases the current as depicted in Figure 7(Bi). Conversely, if positively charged target molecules bind with the biorecognition elements, the holes are depleted and the current is decreased as depicted in Figure 7(Bi). Different types of nanomaterials can be deposited on the sensor surface, such as silicon nanowires [96, 97], carbon nanotubes [98, 99, 100] and graphene [101, 102, 103, 42], for detecting a variety of biological analytes such as proteins, nucleic acids, ions and small molecules, respectively.
# A Gamified AI-Driven System for Depression Monitoring and Management
## 4 Results: Simulation-Based Evaluation of Depression Detection Models
### Depression Scoring and Classification Algorithm  
To assess depressive states, we followed a structured process involving data fusion, composite scoring, and supervised classification:  
1. Feature aggregation: Each synthetic user session included behavioral (such as game accuracy and response latency), cognitive (such as baseline cognitive score, attention span), and contextual (such as temperature and humidity) variables.
2. Weighted composite scoring: A continuous depression score \(D_{i}\) was calculated for each user session using the following equation: \[D_{i}=\sum_{j=1}^{n}w_{j}\cdot x_{ij}\]  
Figure 6: Confusion matrices for each classifier, showing predicted vs. actual labels. Random forest and neural network both achieve high accuracy, with minimal false negatives. Logistic regression shows more misclassifications, particularly in the depressed class.  
where \(x_{ij}\) is the value of feature \(j\) for session \(i\), and \(w_{j}\) is its empirically assigned weight based on relevance in the literature. For instance, mood variability and energy received higher weights, while environmental features were assigned lower weights.
3. Normalization: Depression scores \(D_{i}\) were min-max scaled to the \([0,1]\) range to standardize the output across sessions.
4. Label assignment: The normalized scores were binarized using the dataset median. Sessions with scores above the median were labeled as \"depressed\", while those below were labeled as \"non-depressed\". This binarization enabled the construction of a balanced binary classification task.
5. Model training: Supervised machine learning models--including random forest, logistic regression, and neural networks--were trained to predict the binary depression label using the complete feature set. We applied stratified sampling and cross-validation to ensure robustness and generalizability.