esa-sceva/satcom-chunk-collection huggingface dataset:
# Efficient Gamma-Based Zero-Reference Deep Curve Estimation for Low-Light Image Enhancement
## 3 Methodology
### A Lightweight Neural Network  
Based on the aforementioned GCA attention mechanism and GLE formula, we design a lightweight neural network for low-light image enhancement. The architecture of our network is depicted in Figure 4, which consists of five convolutional layers integrated with two channel reconstruction modules. Regarding the convolutional layer design, the first four layers employ 3 \(\times\) 3 convolutional kernels and maintain 16 feature channels to extract comprehensive feature representations. Each of these layers is followed by a ReLU activation function to introduce nonlinear transformations. The final layer utilizes a 3 \(\times\) 3 convolutional kernel to reduce the feature dimensionality to three output channels, with a Tanh activation function applied to constrain the output range within [\(-\)1, 1]. The two channel reconstruction modules are strategically positioned after the second and fourth convolutional layers. These modules implement our GCA channel attention mechanism through global average pooling combined with softmax activation, effectively enhancing the network's selective capability toward information-rich channels. Subsequently, conditional value amplification is applied, selectively magnifying all positive parameter regions by a factor of 10 while preserving negative value regions unchanged. This asymmetric approach extends the final parameter \(g\) output range to [\(-\)1, 10], thereby enabling enhanced exposure suppression capabilities. The network ultimately outputs a three-channel mapping \(g\), which is used for pixel adjustment across the R, G, and B channels, resulting in the enhanced image. The proposed model, with a significantly reduced parameter count of merely 8 K compared to the 80 K parameters of the Zero-DCE model, thereby gains a substantial advantage in terms of execution efficiency. The experimental results demonstrate that this network architecture achieves an optimal balance between computational efficiency and parameter estimation accuracy, providing a practical and effective solution for low-light image enhancement.  
Figure 4: Overview of our proposed neural network. The input and output images processed by this model are both in the RGB color space. During training, the parameter \(g\) is updated through zero-reference loss functions. During testing, parameter \(g\) is first estimated using our neural network, and then the brightness is adjusted pixel-wise for the R, G, and B channels of the original image using the proposed GLE curve. E is the default exposure parameter for exposure control loss, set to 0.6.
## 6 Using MEC as Enabler for Vehicular Applications
### Video Streaming
When a vehicle requires information stored at a neighboring RSU, the car obtains it from the related MEC server of the RSU. Alternatively, the MBS distributes the desired information to the RSU through the backbone network. Authors, in [177], build a V2V interacting method by predicting the passage distance between two moving cars. They proposed a clustered video-caching technique and calculated the likelihood of the V2V connectivity using the Kolmogorov equation [178]. Next, depending on buffering and delay requirements, they opt to use either V2V or non-V2V connections. Caching occurs at MEC servers. Additionally, they utilize this algorithm to derive the matrix for the ideal buffering approach in order to maximize the buffering function. The results illustrate that the mentioned factors have a substantial impact on the buffering method and that the suggested algorithm outperforms other strategies [177]. Researchers, in [179], build a three-layer hierarchical cooperative cache architecture to fully utilize the storage of MBSs, RSUs, and vehicles. The backbone network and MBSs are both part of the core layer, and wired links are used to interconnect the MBSs to the RSUs. The cooperative RSU layer consists of RSUs dispersed over various locations and connected together via wireless networks. Vehicles operating in various locations are included in the vehicle layer. Memory is available in MBSs, RSUs, and vehicles to cache a specific amount of data. When data are requested, if the car has the content already, it will pull it right out of its cache. If not, the nearest RSU will receive the query. The host RSU queries the associated RSUs if it does not have the content stored. The request is directed to the core network if no RSUs hold the requested data. Enhanced usage of MEC buffering for 360\({}^{\circ}\) video streaming with a dynamic Viewport (VP) is introduced in [180]. A VP is the portion of the 360\({}^{\circ}\) video scene that one is actually seeing at a time, depending on the orientation. The researchers suggest an effective method for using the MEC buffer to identify the best scene tiles to be transmitted to cover the VP. The novelty of the algorithm comes from substituting tiles with near-equal copies, thus minimizing the queries to the remote CS by preloading tiles in MEC servers. Table 6 presents the summary of papers on applications of MEC-assisted VNs.
# Assessment of Quality and Safety of Farm Level Produced Cheeses from Sheep and Goat Milk
## 3 Results
### Milk Quality and Safety
#### 3.1.2 Somatic Cells Analysis  
Somatic cell count is widely used for evaluating milk quality. During the period of study, from April to September, the mean value of SCC in sheep and goat milk has not changed significantly (\(p>0.05\)). The highest number of SCC in sheep milk was detected on July 5.79 \(\pm\) 0.53 log\({}_{10}\) SCC and the lowest number of SCC was detected in April 5.51 \(\pm\) 0.52 log\({}_{10}\) SCC.  
In goat milk the highest SCC was determinate in September 4.59 \(\pm\) 0.95 log\({}_{10}\) SCC and the lowest 4.31 \(\pm\) 0.33 log\({}_{10}\) SCC in April (Table 3). Mean value of somatic cell count corresponds to the increased prevalence of mastitis during the summer months. The mean value of SCC during the research period was in sheep milk 5.68 \(\pm\) 0.82 log\({}_{10}\) SCC and 4.44 \(\pm\) 0.67 log\({}_{10}\) SCC in goat milk.  
The month of milking in both cases (sheep milk and goat milk) influenced the content of most milk components, but no changes in SCC (\(p>0.05\)) during season research were found. Total solids in milk were found to be lower in goat milk with high SCC. It is well accepted that an increase of SCC causes a decrease in the concentration of lactose in both sheep milk and goat milk. In this study, the highest SCC was determined in August and September, when the prevalence of intramammary gland infection (IMI) is higher. The results show that the highest content of total proteins was recorded in the month with a higher SCC. This condition may be due to the lactation phase. In general, the protein content is high at the beginning of the lactation period, then decreases in the middle of the lactation period, and then increases again at the end of the lactation period. Results showed that the lowest value of somatic cells in goat milk was detected at the beginning of the study period 4.31 \(\pm\) 0.33 log\({}_{10}\) SCC and the highest number of SCC was measured on September 4.59 \(\pm\) 0.95 log\({}_{10}\) SCC, what can be attributed to the fact that in the summer months there is an increase in the prevalence of IMI, while in general the highest occurrence of diseases is recorded in August and September.
# An Embedded System Based on Raspberry Pi for Effective Electrocardiogram Monitoring
## 3 The ECG Circuit Design
### The Block Diagram of the Circuit  
The main stages of the ECG circuit designed in this paper are shown in the block diagram represented in Figure 1. The ECG signal is taken from the human body through three leads connected to Right Arm (RA), Left Arm (LA), and Right Leg (RL).  
The first stage represents the combination of the instrumentation amplifier (INA122) that is used to collect the main signal from the RA and LA through the right and left electrodes, and the right leg is connected through a third electrode to the RLD.  
The second stage is a high pass filter used to reduce the high frequency noise, then a low pass filter used for low frequency noise reduction, and a notch filter used to filter out the noise coming from the power lines. In the next stage, we have added a summing amplifier to add some DC-offset to make the signal compatible with analog to digital conversion for extra processing. Then we have added an extra amplifier for extra amplification of the signal. The final stage consists of a peak detector to detect R-peaks for measuring heart rate. We also have added buffers between stages to reduce loading effect.
# Investigating Master-Slave Architecture for Underwater Wireless Sensor Network
## 4 System Model
### Basic Terminologies  
Following are the basic terminologies used in our proposed protocol ERPMSA-UWSN protocol.  
* Master nodes: Master nodes are those nodes that fulfill the requirements of the Master Selector function (MSF) and are eligible for the forwarding data packets.
* Slave nodes: Nodes other than masters are slave nodes and are not eligible for forwarding data packets.
* Anchor nodes: Nodes which are statically bounded at the bottom of the water, also known as data collectors or source nodes.
* Relay nodes: Nodes which are suspended in water and work as 3 rd party between anchor nodes and sink nodes.  
Figure 1: Selection of next PFN in WDFAD.  
* Sink nodes: Sink nodes are the receiver/destination nodes and they float on the surface of the water. Their responsibility is to collect data packets from sensor nodes and forward them to base stations through a radio link.
* Transmission range of node S: This refers to the omnidirectional distance from source node S (Xs, Ys, Zs) which currently forwards the packet p until and unless the packet is transmitted.
* Void hole: A state where the forwarding node cannot find any other node to further forward a packet.
* Energy hole: A scenario where the forwarding node lacks the required energy for forwarding a data packet.
* Eligible Neighbors (\(En_{i}\)) of Node i: Nodes spanning within the transmission range of a given node \(i\). Let's suppose \(N\) represents a set of nodes in a network \[N=n_{1},n_{2},n_{3},.......,n_{k}\] (1) Therefore, the eligible neighbors of a Node \(i\) can be illustrated as \(En_{\text{i}}\subseteq N\) \[En_{i}=\left\{j\in N\wedge Dist_{j}^{i}\leq R_{r}^{i}\right\}\] (2) where (\(Dist_{j}^{i}\)) is the Euclidean distance between node \(i\) (\(x_{i},y_{i},z_{i}\)) and node \(j\) (\(x_{j},y_{j},z_{j}\)) in a 3-dimensional Euclidean space: \[Dist_{j}^{i}=\sqrt{\left(x_{i}-x_{j}\right)^{2}+\left(y_{i}-y_{j}\right)^{2}+ \left(z_{i}-z_{j}\right)^{2}}\] (3)
* Potential Forwarders (PFi) for a Node \(i\): Nodes lying in the transmission range \(R_{r}^{s}\) and with their depth (\(d_{j}\)) less than the depth (\(d_{i}\)) form the potential forwarders for a node i, as given below: \[Pf_{i}\subseteq En_{i}\] where \[PF_{i}=\left\{j\in En_{i}\wedge d_{i}<d\right\}\]
* Potential Forwarding Zone (PFZ): The Potential Forwarding Zone (PFZ) creates a hemispherical zone, where the radius is similar to \(R_{r}^{s}\) and the distance to the sink for each point of PFZ is lower than that of the source node. The PFZ is the sub-region of \(R_{r}^{s}\) for node S and the nodes residing in the zone are Potential Forwarder Nodes (PFNs), i.e., the subsequent forwarders for the packet p.  
All abbreviations/symbols used in this manuscript are listed in Table 2.
# Finite Element Analysis of Proposed Self-Locking Joint for Modular Steel Structures
## 4 Validation of FE Model  
To verify FE modeling techniques, a comparison between the FE model and experimental results has been conducted. For this purpose, experimental results obtained by [18] were used. The moment-drift ratio data were obtained from reference study by utilizing image digitization technique, while FE model data were obtained by importing output data from ABAQUS to Microsoft Excel. The specimen configuration, support conditions and its geometrical section and FE idealization, are shown in Figure 3a. The support conditions of these experimental specimens were pinned at both ends of the column, and beams' ends were free to move in the direction of the load. To simulate actual behavior in FE, appropriate boundary conditions were applied to the model as illustrated in Figure 6a. Mechanical properties of materials of the experimental specimen are shown in Table 4. The theoretical tensile yield strength of 640 MPa and ultimate tensile strength of 880 MPa was used for bolts [31].  
In reference study [18], the design assumptions of the specimen were considered that sections of beams would affect the seismic behavior of joints, as most plastic deformation occurred on the root of the beam. This study focused on the global and local performance of column beam joints based on these design criteria. The results of FE modeling and experimental were compared in terms of the moment and drift ratio (M-0) curve as shown in Figure 6b. The overall response of the FE curve agrees well with the experimental data. This M-0 curve is commonly categorized by an initial stiffness phase that is followed by the second phase of much-reduced stiffness. From the observation, both curves initially remain in an elastic state up to a moment of 218 kNm. Afterward, both curves entered a nonlinear state, after that moment peaked 289 kNm for experimental and 290 kNm for FE model. Afterward, both curves showed a descending trend.  
In terms of physical observation, a gap between upper and lower columns was observed at the outer region of connection boxes in FE model; this was identical to what was observed in experimental work. A gap of 3 and 3.58 mm produced between upper and lower connection boxes in experimental and FE model respectively as shown in Figure 6c. FE model produced local buckling at web and flange of root ceiling beam similar to observed in the experimental test as illustrated in Figure 6d. This behavior shown good agreement results between experimental and FE studies. Therefore, FE modeling used in this study was able to well simulate the actual performance of the experimental work.  
Figure 6: Comparison between experimental observations [18] and FE model. (**a**) Test setup. (**b**) Moment vs. drift ratio curve. (**c**) Opening between lower and upper connection box. (**d**) Local buckling at root of ceiling beam.
# Adipose Stem Cell Response to Borophosphate Bioactive Glass
## 2 Materials and Methods
### Cell Culture  
#### 2.3.1 Adipose Stem Cells  
ASCs were prepared by thawing frozen vials of approximately 1 \(\times\) 10\({}^{6}\) cells (Obatala Sciences, LLC, New Orleans, LA, USA) into 150 cm\({}^{2}\) culture plates (Nunc, Rochester, NY, USA) in 20 mL complete culture media (CCM) consisting of alpha minimum essential media (\(\alpha\)-MEM; Sigma; St. Louis, MO, USA), 10% fetal bovine serum (FBS; VWR, Dixon, CA, USA), 1% 100x L-glutamine (Sigma, St Louis, MO, USA), and 1% 100\(\times\) antibiotic/antimycotic (Sigma). After 24 h of incubation at 37 \({}^{\circ}\)C humidified and 5% CO\({}_{2}\), media was removed, and the adherent, viable cells were washed twice with phosphate-buffered solution (PBS; Sigma) and harvested using 0.25% trypsin/1 mM Ethylenediamine-tetraacetic acid (EDTA; Sigma, St Louis, MO, USA). ASCs were then plated at 100 cells/cm\({}^{2}\) in CCM. The media was changed every 3-4 days, and subconfluent cells (\(\leq\)70% confluent) from three separate donors between passages 2 and 6 were used for all experiments. Donors were Caucasian females aged 28, 34, and 34 with BMIs of 25.65, 22.03, and 20.34, respectively.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Reagent** & **Vendor** & **Location** \\ \hline Live/Dead stain & Fisher Scientific & Rochester, NY, USA \\ \hline Na2 CO3 & Alfa Aesar & Ward Hill, MA, USA \\ \hline NaPO3 & Shanghai Muhong Industrial Co, Ltd. & Shanghai, China \\ \hline Oil Red O & Sigma & St Louis, MO, USA \\ \hline Osteogenesis Media & Lonza & Walkersville, MD, USA \\ \hline PBS & Sigma & St Louis, MO, USA \\ \hline Quantibody Array & RayBiotech & Norcross, GA, USA \\ \hline Trypsin/EDTA & Sigma & St Louis, MO, USA \\ \hline \end{tabular}
\end{table}
Table 1: _Cont._  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Glass Designation** & **Na2O** & **CaO** & **B2O3** & **P2O5** & **Tm (\({}^{\circ}\)C)** \\ \hline X0 & 14.2 \(\pm\) 0.1 & 25.5 \(\pm\) 0.1 & 0.0 \(\pm\) 0.3 & 60.4 \(\pm\) 0.5 & 1050 \\ \hline X40 & 14.1 \(\pm\) 0.1 & 24.2 \(\pm\) 0.2 & 40.6 \(\pm\) 0.2 & 21.2 \(\pm\) 0.1 & 1150 \\ \hline X60 & 15.2 \(\pm\) 0.1 & 24.3 \(\pm\) 0.2 & 60.5 \(\pm\) 0.2 & 0.0 \(\pm\) 0.0 & 1000 \\ \hline \end{tabular}
\end{table}
Table 2: Compositions of the BPBGs used in this study (mol %) as determined by ICP-OES analysis and their respective melt temperatures (Tm). Compositions collected in triplicate and standard deviation reported [38].  
2.3.2. Dermal Microvascular Endothelial Cells  
Human dermal microvascular endothelial cells (HMVEC-d, pooled donors) were obtained from Lonza (Walkersville, MA, USA). HMVECs were grown under standard conditions in Endothelial Cell Basal Medium-2 (Lonza; Walkersville, MA, USA), and media was changed every 3-4 days.
## 4 Discussion
While the general protocol did not lead to significant improvements in jump power for all participants, a more detailed analysis revealed that a subgroup of athletes showed positive responses, particularly in the squat jump (S)) compared to the countermovement jump [12]. This difference can be attributed to biomechanical similarities between the conditioning activity and SJ, which typically engage similar muscle groups and movement patterns. Furthermore, the lack of a universally positive response among the study group highlights several factors that may influence the effectiveness of PAPE protocols. These include the specificity of the conditioning activity relative to the task performed, athletes' muscle fiber composition, and even the timing and intensity of the conditioning activity [37]. While accommodating resistance may be a valuable tool in enhancing performance, its application requires careful adjustment to each athlete's individual characteristics and capabilities [12]. The results underscore the importance of a holistic approach to training and conditioning athletes, prompting a rethink of the commonly used \"one size fits all\" methodology in training programs. Instead, they advocate for a more nuanced strategy that considers the unique physiological and biomechanical profiles of each athlete, thereby optimizing athletic outcomes while potentially reducing the risk of injuries associated with inappropriate training loads or activities that are not tailored to individual needs. A review of the literature on the effects of PAPE highlights the significant role of various factors such as the type and intensity of conditioning activity, the time elapsed between its completion and the main effort, and the individual physiological characteristics of athletes in shaping the effectiveness of this phenomenon. Studies on PAPE are crucial for better understanding the physiological processes occurring in muscles after specific types of physical activity and for optimizing training protocols in sports.
# Machine Learning in Beyond 5G/6G Networks--State-of-the-Art and Future Trends
## 4 Beyond 5G/6G Applications and Machine Learning
### Reinforcement Learning
#### 4.3.9 Visible Light Communication  
In [124], the authors propose a DQN based multi-agent multi-user algorithm for hybrid networks for power allocation. These networks are composed of radio frequency (RF) and visible light communication (VLC) access points (APs). The users are capable of multi-hopping, which can link RF and VLC systems in terms of bandwidth requirements. In the proposed DQN algorithm, each AP is considered an agent and so the transmit power needed for users is optimized by an online power allocation strategy. Simulation results demonstrate faster median convergence time training (90 shorter than typical Q-Learning based algorithm) and convergence rate is 96.1% (whereas conventional QL-based algorithm's convergence rate in 72.3%). In [125], a multi-agent Q-learning algorithms is proposed for power allocation strategy in RF/VLC systems. In these systems, in order to ensure QoS satisfaction, the transmit power at the Aps needs to be optimized. Simulation results demonstrate the effectiveness of the proposed Q-learning based strategy in terms of accuracy and performance.
# A Novel Fault-Tolerant Aware Task Scheduler Using Deep Reinforcement Learning in Cloud Computing
## 5 Simulation and Results
### Calculation of Energy Consumption for DRFTSA Using Large Dataset by Varying Number of VMs  
In this section, we present the next scenario of generated energy consumption for DRFTSA with a large dataset and by varying the number of VMs. In this scenario, we linearly assigned different numbers of VMs, i.e., 50 VMs for 700 tasks, 60 VMs for 800 tasks, 70 VMs for 900 tasks, and 80 VMs for 1000 tasks. Generated energy consumption for PSO with 700, 800, 900, 1000 tasks is 82.17, 75.56, 84.26, 90.38, respectively. Generated energy consumption for GA with 700, 800, 900, 1000 tasks is 85.12, 87.03, 89.21, 92.77, respectively. Generated energy consumption for ACO with 700, 800, 900, 1000 tasks is 88.48, 82.66, 89.35, 94.17, respectively. Generated energy consumption for DRFTSA with 700, 800, 900, 1000 tasks is 56.18, 63.81, 71.14, 60.11, respectively.  
From Table 11 and Figure 11, it is evident that energy consumption of DRFTSA compared to various approaches is greatly minimized with the considered dataset and VMs.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**No. of Tasks** & **PSO** & **GA** & **ACO** & **DRFTSA** \\ \hline
700 & 82.56 & 86.16 & 93.42 & 48.89 \\
800 & 84.39 & 87.99 & 97.19 & 59.18 \\
900 & 93.57 & 97.17 & 98.42 & 68.74 \\
1000 & 112.68 & 108.99 & 134.77 & 100.67 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Energy consumption for DRFTSA using large dataset using fixed number of VMs.  
Figure 10: Calculation of energy consumption for large dataset by using fixed number of VMs.
## 2 Related Work
### Disaster Warning System  
Disaster warning systems are used to broadcast warning messages to the public and, thus, warn people of imminent disasters. [PERSON] _et al_. [7] developed a seismic early warning alert system that can forewarn an urban area of a forthcoming strong quake, so that people could take appropriate actions and respond to that situation effectively. [PERSON] _et al_. [8] developed a warning system, named OpenStreetMap (OSM), which provides a specific service to notify people about warning information and to help them determine the shortest path for evacuation. [PERSON] _et al_. [9] utilized GSM information to build a disaster early warning network and adopted the network to deliver warning messages. When the proposed disaster warning system receives alert messages from the warning network, it will make a loud noise, turn on warning lights and broadcast messages to notify people. [PERSON] _et al_. [10] proposed a disaster emergency warning system based on the DVB-T (Digital Video Broadcasting-Terrestrial) standard. [PERSON] _et al_. [11] and [PERSON] _et al_. [12, 13] used social media, such as Twitter and Facebook, to collect disaster information. Based on the collected information, they then broadcasted warning messages to registered users through smart phones. Although these warning systems can send alerts to the public, none of them adopted CAP standards for encoding alert messages. Because their warning messages are not machine-readable, these warning systems are incompatible with smart devices to take appropriate actions to keep people away from potential danger. ADRS is a further extension of our previous research on cyber-physical elements of a disaster-prepared smart environment [14, 15]. Compared to our previous work, this paper mainly focuses on the design and implementation of a proof of concept prototype to automatically perform emergency tasks when an earthquake happens.