esa-sceva/satcom-chunk-collection huggingface dataset:
# Performance Analysis of DF Relay-Assisted D2D Communication in a 5G mmWave Network
## 1 Introduction
### Notations  
The notations used in this paper are given as follows. The notation \(\bigoplus\) represents the bitwise binary XOR operation. \(\mathbb{P}\) denotes the probability function. The notation \(f_{y}(.)\) is used to denote the probability distribution function (PDF) for the random variable \(y\), see Table 1.  
The remainder of the article is organized as follows. Section 2 describes the system model where the basic assumptions along with the path loss model and the information model are discussed. Section 3 gives the problem formulation and analysis. Expressions for the coverage probability of D2D users and the relay selection technique are formulated in this section. In Section 4, the simulation results are analyzed and discussed. Finally,  
\begin{table}
\begin{tabular}{p{42.7 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Sl. No.** & **Symbols** & **Significance** \\ \hline
1 & \(f_{c}\) & Operating frequency \\ \hline
2 & \(d\) & Distance between T and R \\ \hline
3 & \(\alpha\) & Path loss exponent \\ \hline
4 & \(AT\) & Interference due to atmospheric absorption \\ \hline
5 & \(\chi_{\sigma}\) & Log-normal shadow fading having zero mean \\ \hline
6 & \(\sigma\) & Standard deviation (in dB) \\ \hline
7 & \(P_{C},P_{D},P_{T},P_{S}\) & Cellular power, D2D power, circuit power, and scattering power, respectively \\ \hline
8 & \(h_{D2D},h_{C2D}\) & Channel gain for D2D and the CU to D2D, respectively \\ \hline
9 & \(h_{C2B},h_{D2B}\) & Channel gain for the CU to the BS and D2D to the BS, respectively \\ \hline
10 & \(d_{D2D},d_{C2D}\) & Distance between D2D and the CU to D2D, respectively \\ \hline
11 & \(d_{C2B},d_{D2B}\) & Distance between the CU to the BS and D2D to the BS, respectively \\ \hline
12 & \(n_{D},n_{C}\) & AWGN noise at D2D and the CU users, respectively \\ \hline
13 & \(n_{R},n_{A},n_{B}\) & AWGN noise at the relay node, D2D A, and D2D B, respectively \\ \hline
14 & \(\gamma_{th}\) & Threshold SINR \\ \hline
15 & \(P_{AR},P_{BR}\) & Received power at the relay node from D2D A and D2D users, respectively \\ \hline
16 & \(P_{RA},P_{RB}\) & Received power at D2D A and D2D users from the relay node, respectively \\ \hline
17 & \(P_{SA},P_{SB},P_{CR}\) & Received power at D2D A and D2D users from the BS and from the CU to the relay, respectively \\ \hline \hline \end{tabular}
\end{table}
Table 1: List of notations.  
Section 5 concludes the work with remarks about the proposed work along with a brief discussion about the future scope and directions.
## 1 Introduction
In this article, firstly, we present an integrated wireless sensor and sensing-based cognitive satellite terrestrial network, where the satellite user acts as the sink node in the wireless sensor network for gathering and transmitting sensing data by the satellite network. Meanwhile, the satellite user is also the cognitive SU in the satellite terrestrial network. Subsequently, sensing-based power allocation scheme in the cognitive satellite terrestrial network is introduced for the perfect sensing scenario and the imperfect sensing scenario, which aims to make the cognitive satellite user access the channel seamlessly through intelligent schemes. We use the ergodic capacity of the satellite user to evaluate the performance of the scheme, and the average interference power constraints of the satellite user are introduced to constrain the interference to the terrestrial user. For the nonlinear concave optimization problem of the ergodic capacity, we use the Lagrange duality method [31] to decompose the problem into several parallel sub-problems. Herein, an iterative algorithm for searching the optimal transmission power of the satellite user and the sensing time is proposed. Finally, simulation results show that, comparing the traditional opportunistic spectrum access, the satellite user can get higher ergodic capacities under the presented method. The following sections of the paper are structured as follows: In Section 2, the integrated wireless sensor and sensing-based cognitive satellite terrestrial system model and link budgets are introduced. In Section 3, the sensing-based power allocation scheme is formulated for the perfect and imperfect sensing scenarios, the numerical simulation results are presented in Section 4, and Section 5 concludes the paper.
## 2 Theory and Method
### The Governing Equations
#### 2.2.1 Dissolution Equation  
The previous dissolution equation only considered the effect of concentration, but the effect of temperature is must be considered [41; 43]. The dissolution rate of salt boundary can be expressed as Equation (1), which describes the process of cavern development.  
\[\Phi=\mathrm{C}_{dis}(c_{sat}-c)\cdot\ln\frac{(T-T_{0})^{0.44}}{e} \tag{1}\]  
where \(\Phi\) is the dissolution flux at the salt-brine interface, mol/(m\({}^{2}\cdot\)s); \(\mathrm{C}_{dis}\) is the coefficient of dissolving rate, m/s; \(c_{sat}\) is the concentration of saturated brine, mol/m\({}^{3}\); \(c\) is the concentration of brine, mol/m\({}^{3}\); \(T\) is the temperature of the salt cavern, K; and \(T_{0}\) is the freezing temperature of water, K.
## 3 Modeling and Data Sources
### Data Source  
#### 3.3.1 Study Area  
As shown in Figure 3, Yunnan Province, located in the southwest frontier of China, spans from 21\({}^{\circ}\)8\({}^{\prime}\)32\({}^{\prime\prime}\) N to 29\({}^{\circ}\)15\({}^{\prime}\)8\({}^{\prime\prime}\) N latitude and from 97\({}^{\circ}\)31\({}^{\prime}\)39\({}^{\prime\prime}\) E to 106\({}^{\circ}\)11\({}^{\prime}\)47\({}^{\prime\prime}\) E longitude. The province stretches approximately 865 km east-west and about 990 km north-south, covering an area of around 394,000 square kilometers. This accounts for 4.11% of China's total area, ranking it eighth among all provinces. Yunnan is characterized by its subtropical highland monsoon climate, distinguished by small annual but large diurnal temperature variations and a clear distinction between the dry and wet seasons. Average temperatures in July range from 20 \({}^{\circ}\)C to 23 \({}^{\circ}\)C, while in January, they range from 7 \({}^{\circ}\)C to 11 \({}^{\circ}\)C [39].  
Yunnan has always been a focal point for forest fire prevention in China due to its challenging fire prevention situation. It is one of the provinces richest in forest resources in the country, boasting the third-largest forested area in China, accounting for 10% of the national forest area, approximately 2273 hectares. The province has a high forest coverage rate of 59.30% and a greening rate of 67.82%. The total standing volume of live trees is about 1.913 billion cubic meters, with forest reserves estimated at 1.895 billion cubic meters.  
Figure 3: Location of Yunnan Province (YN) in China.  
Due to lower rainfall during the dry season (November to April), which accounts for only 15% of the annual rainfall, Yunnan is a high-incidence area for forest fires and has been designated as a national key fire prevention region [40].
# Space-Based Displacement Monitoring of Coastal Urban Areas: The Case of Limassol's Coastal Front
## 3 Results  
### PSI Classification and Trends of PSI Results  
Following the above methodology, the geographic information (location) of the corresponding PSs and the vertical displacements in millimeters/year and their statistics were extracted. Concerning the classification of the PSs values, it was held in ten (10) classes for visualization purposes. Each class was equally distanced by 1 mm, ranging from \(-\)5 mm to 4 mm within the studied period. For further analysis, the PSs included in a 200 m buffer zone were analyzed for each structure, and the mean value was determined for the displacement in the vertical component in each case.  
As shown in Figures 4 and 5, where the location distribution of the PSs in the buffer zone is presented on an excerpt of a map, there was a significant variance in the point density in each case. Table 5 contains the quantity of PSs within the 200 m buffer zone around each construction area, as well as the construction year. This is ascribable to the starting date of each construction and the presence of the buildings in all Sentinel-1 scenes, according to the master date (17 July 2021). Thereby, the buffer zone of 200 m around each building was created, including PSs, as presented in Figure 8. For each construction, statistics data describing the mean, minimum, and maximum value of the vertical subsidence were computed. For the presentation of the PSI results, a categorization of the buildings into skyscrapers (<100 m) and tall buildings (<100 m) according to the height component [55] was carried out.
# Modulation of Inflammatory Cytokine Production in Human Monocytes by cGMP and IRAK3
## 2 Results  
### Sub-Nanomolar cGMP Suppresses Cytokine Production in LPS-Challenged Monocytes  
As the effects of low intracellular levels of cGMP on cytokine production have not been investigated, we used the membrane permeable analogue of cGMP (8-Br-cGMP) at a wide range of concentrations from sub-femtomolar to micromolar, to assess cGMP impact on inflammation by quantifying NF\(\kappa\)B activity and cytokine production in monocytic cells. We used THP-1-BLUE cells which are derived from human THP-1 monocyte cell line and are stably integrated with an NF\(\kappa\)B-inducible secreted embryonic alkaline phosphatase (SEAP) reporter construct ([[https://www.invivogen.com/seap-reporter-gene-system](https://www.invivogen.com/seap-reporter-gene-system)]([https://www.invivogen.com/seap-reporter-gene-system](https://www.invivogen.com/seap-reporter-gene-system)) last accessed 30 September 2021) to study NF\(\kappa\)B activity. LPS stimulation enhanced NF\(\kappa\)B activity in THP-1-BLUE cells in a dose- and time-dependent manner (Supplemental Figure S1A). Stimulation with 0.01 \(\upmu\)g/mL LPS only caused significant increases in NF\(\kappa\)B activity at 18 h and 24 h (Supplemental Figure S1A). At LPS concentrations between 0.1 to 10 \(\upmu\)g/mL, NF\(\kappa\)B activity remained unchanged in the first 4 h post-treatment, then significantly increased (Supplemental Figure S1A). The highest increases in NF\(\kappa\)B activity were observed 24 h post-treatment with 1-10 \(\upmu\)g/mL LPS (Supplemental Figure S1A). Treatment with at least 0.1 nM (10\({}^{-10}\) M) 8-Br-cGMP significantly decreased LPS-induced NF\(\kappa\)B activity, while 8-Br-cGMP at less than 0.1 nM had no effect on NF\(\kappa\)B activity (Figure 1A). Membrane permeable cGMP had no effect on NF\(\kappa\)B activity in cells that have not been stimulated with LPS (Supplemental Figure S1B).  
Activation and nuclear translocation of NF\(\kappa\)B transcription factor triggers the transcription of inflammatory genes including IL-6 and TNF-\(\alpha\). To see if low levels of membrane permeable cGMP modulate cytokine production, we measured TNF-\(\alpha\) and IL-6 protein secretion following LPS stimulation of THP-1 monocytes. 8-Br-cGMP at sub-nanomolar (0.1 nM) or higher (100 nM) concentrations suppressed TNF-\(\alpha\) and IL-6 production in a dose-dependent manner (Figure 1B,C). Like NF\(\kappa\)B activation (Figure 1A), femtomolar concentrations of 8-Br-cGMP did not affect TNF-\(\alpha\) and IL-6 protein expression and secretion (Figure 1B,C).
# Thin Film Encapsulation for RF MEMS in 5G and Modern Telecommunication Systems
###### Abstract  
In this work, SiN\({}_{\text{x}}\)/a-Si/SiN\({}_{\text{x}}\) caps on conductive coplanar waveguides (CPWs) are proposed for thin film encapsulation of radio-frequency microelectromechanical systems (RF MEMS), in view of the application of these devices in fifth generation (5G) and modern telecommunication systems. Simplification and cost reduction of the fabrication process were obtained, using two etching processes in the same barrel chamber to create a matrix of holes through the capping layer and to remove the sacrificial layer under the cap. Encapsulating layers with etch holes of different size and density were fabricated to evaluate the removal of the sacrificial layer as a function of the percentage of the cap perforated area. Barrel etching process parameters also varied. Finally, a full three-dimensional finite element method-based simulation model was developed to predict the impact of fabricated thin film encapsulating caps on RF performance of CPWs.  
Keywords:5G; RF MEMS; thin film encapsulation; silicon nitride; oxygen plasma etching +
Footnote †: journal: Sensors
## 3 Preliminaries
### Elais Gamma Encoding  
The run-length sequence that we obtain through the RLE process cannot be stored or transmitted since we need to convert the same to a sequence of bits. We can use a fixed-length encoding approach or a variable-length approach to transform the RLE sequence into a series of bits. The number of bits used for representing each of the sequences in RLE will be determined by the number of bits required to represent the maximum value in the sequence. For example, if \(Q\) is the maximum run-length size, then \(\lceil Log_{2}\;(Q+1)\rceil\) number of bits should be used to store each value in the RLE sequence. The fixed-length encoding is not preferred since we may be wasting several bits while encoding small integers. This motivated us to use some variable-length encoding approaches in the RLE encoding process. The Elias gamma encoding process (EGE) is one well-known variable-length encoding approach.  
Let us assume that we have an integer value \(G\), the corresponding binary value is \(G^{B}\), where \(|G^{B}|=N\). In this case, if we use Elias gamma encoding, we will embed \((N-1)\) number of 0s as the prefix of \(G^{B}\) to obtain the corresponding Elias gamma-encoded sequence.  
Consider the RLE sequence given in Equation (5); the binary values corresponding to 6 is 110, 5 is 101 and 10 is 1010. Therefore, the Elias gamma encoded sequence \(\hat{B}^{EGE}\) will be as follows:  
\[\hat{B}^{EGE}=(1\;00110\;00101\;0001010) \tag{6}\]  
The green-colored bit sequence indicates the Elias gamma code corresponding to the count of 1 s, and the red-colored bit sequence shows the Elias gamma code corresponding to the count of 0 s.  
The readers may note that we have modified the existing Elias gamma encoding process in this work so that we shall embed a sequence of bits in the Elias gamma-encoded sequence itself.
# Greenhouse Residues' Potential for Biogas Production
## 3 Results
### BMP Evaluation
Therefore, transferring the fresh residue biomass to a central biogas plant would be economically and environmentally unsustainable, while generating biogas on-site would be an attractive alternative. [PERSON] et al. [47] concluded that exploiting the plant residues (leaves and suckers) of a hydroponic greenhouse for energy production via a combined heat and power (CHP) engine and managing the energy supply via a hybrid CHP battery system could partially cover the facility's energy demands. Furthermore, incorporating the biomass from the removal of the whole plant would increase the total biomass and contribute to increasing the autonomy level of the energy management system. \begin{table}
\begin{tabular}{l c c c c c c c c c c c c} \hline
**Tons** & **January** & **February** & **March** & **April** & **May** & **June** & **July** & **August** & **September** & **October** & **November** & **December** & **Total** \\ \hline T.S. & 0 & 0 & 27 & 264 & 346 & 437 & 458 & 360 & 275 & 339 & 127 & 0 & 2633 \\ T.L. & 0 & 0 & 7 & 66 & 86 & 109 & 115 & 90 & 69 & 85 & 32 & 0 & 658 \\ T.St. & & & & & & & & & & & 425.7 & & 426 \\ C.S. & 8 & 264 & 500 & 625 & 495 & 155 & 213 & 471 & 317 & 149 & 74 & 0 & 3272 \\ C.L. & 1 & 47 & 88 & 110 & 87 & 27 & 38 & 83 & 56 & 26 & 13 & 0 & 577 \\ C.St. & & & & & & & 113 & & & & & 113 & & 225 \\ \hline Total & 9 & 311 & 622 & 1065 & 1015 & 841 & 823 & 1004 & 718 & 600 & 672 & 0 & 7567 \\ \hline \end{tabular}
\end{table} 
Table 3: Annual production of residual biomass in Thrace Greenhouses S.A in tons of fresh matter (FM). Figure 2: BMP of the various residue types along time expressed in g VS (**a**) or g COD (**b**). Therefore, a detailed recording of the quantities of all types of residues removed per month in the hydroponic greenhouse was conducted per month (Table 3). During December, the greenhouse was prepared for the next crop season. Cucumbers were planted first (in January and February), and tomatoes were planted in March. The cucumbers were renewed at the end of May and the beginning of June; therefore, the whole crop plant was removed (recorded as \"stalk\" in Tables 1 and 2). Both plants (tomatoes and cucumbers) were removed in November when the crop season was over. Therefore, the leaves and suckers were generated at a rate corresponding to the stage of crop growth. The highest amount of biomass was recorded between April and August, with a slight decline in June and July because of the cucumber replantation. The maximum methane production per month was calculated based on the BMP of each residual biomass type (Figure 3b) and their quantity (Table 3). Following the trend of the amount of biomass, the highest methane production was expected between April, May, and August (Figure 4). However, since the tomato plants were removed massively in November, there was a considerable quantity of residues (tomato stalks) available having a high BMP compared to the BMP of leaves (Figure 3b).
# Real-Time Farm Surveillance Using IoT and YOLOv8 for Animal Intrusion Detection
## 3 Results and Experiments  
A variety of critical indicators are used to study machine learning model performance, which, when combined, give a thorough assessment of the models' efficacy. These critical indications include the F1_score, accuracy, precision, recall, and Confusion Matrix, each of which serves a distinct purpose in the evaluation process. Matrix of Confusion: The Confusion Matrix, which has a tabular form, is an important tool for measuring a model's classification accuracy. This matrix is made up of four fundamental parts:  
* Real Positives (TP): These are the cases that were appropriately identified as positive.
* True Negatives (TN): These are examples of situations that were accurately categorized as negative.
* False Positives (FP): These are situations in which Type I errors have led to an inaccurate positive classification.
* False Negatives (FN): These are cases that are Type II mistakes and are mistakenly labeled as negative.
* Accuracy: The accuracy Equation (1) calculates the ratio of accurately predicted instances to the total number of instances to determine the general correctness of the model's predictions. It gives an overall evaluation of the model's performance. \[Accuracy\ =\frac{Number\ of\ Correct\ Predictions}{Total\ Number\ of\ Predictions}\] (1)  
Figure 12: Training the ML models.  
Precision: Precision is defined as the fraction of actual positives out of all positive assertions. Precision assesses the accuracy of optimistic forecasts.  
\[Precision\ =\ \frac{True\ Positives}{True\ Positives+False\ Positives} \tag{2}\]  
Recall: The proportion of true positives among all actual positives. The model's recall evaluates the efficiency with which it detects cases that are positive.  
\[Recall\ =\ \frac{True\ Positives}{True\ Positives+False\ Negatives} \tag{3}\]  
F1_score: The harmonic mean of recall and precision is the F1_score. It examines the overall performance of the algorithm in question.  
\[F1-Score\ =\ 2\ *\frac{Precision\ *\ Recall}{Precision\ +\ Recall} \tag{4}\]
## 4 Discussion
\begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt} p{142.3 pt}} \hline \hline
**Criterion** & **CNN-Based Models (8 Networks: ResNet, GoogleNet, etc.)** & **Newer Networks (e.g., ViT, Swin Transformer, ConvNeXt, Vision Mamba)** \\ \hline Computational Efficiency & Efficient due to optimized convolutional operations and suitable for medical imaging on standard GPUs. & Require large computational resources, such as high-end GPUs, increasing the inference time. \\ \hline Training Data Requirements & Perform well on small datasets; ImageNet-pre-trained models can be fine-tuned on medical data. & Need large-scale datasets for training due to the absence of spatial inductive bias. \\ \hline Feature Extraction & Possess strong local feature extraction capabilities, useful in detecting small abnormalities in retinal images. & These models focus on global relationships; however, they may miss fine-grained details in small medical images. \\ \hline Generalization to Small Datasets & Work well with limited labeled medical data due to strong hierarchical feature learning. & Prone to overfitting on small datasets without extensive augmentation and regularization. \\ \hline Interpretability & More interpretable with feature visualization tools such as Grad-CAM. & Transformers lack straightforward interpretability, leading to challenges in clinical validation. \\ \hline Availability of Pre-Trained Medical Models & CNN-based models pre-trained on medical imaging datasets (e.g., ImageNet). & Limited availability of pre-trained models for medical imaging; require domain-specific fine-tuning. \\ \hline Localization of Retinal Features & CNNs naturally capture local spatial features (blood vessels, lesions) with small receptive fields. & Transformers analyze entire images at once, which can reduce the accuracy in detecting small retinal abnormalities. \\ \hline Stability and Robustness & CNNs are well tested for medical image classification and generalize well across datasets. & Transformers are newer and need extensive hyperparameter tuning to avoid instability. \\ \hline Adoption in Medical AI & Widely employed in retinal disease diagnosis (e.g., diabetic retinopathy, glaucoma detection). & Limited adoption in medical AI due to high data needs and computational costs. \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Comparison of CNNs and the newer networks used in retinal disease diagnosis. improve access to care. A standardized, reliable method to analyze retinal images is of great interest, and early AI methods seem to hold promise. These technological solutions reduce the need for trained specialists in the primary care setting and enable more efficient screening. Moreover, the promising results derived in the current work support the fact that AI reduces the burden associated with the manual review of fundus images [42] through the use of technology to automatically screen such images for the presence of pathologies, especially for DR detection.