esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Relationship between Whole Blood miRNAs, eGFR, and Prevalent CKD
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \(\beta\) & **95\% CI** & \(p\)**-Value** \\ \hline hsa-miRR-novel-chr7\_76196 & & & \\ \hline Model 1 & \(-\)10.1 & \(-\)18.05 to \(-\)2.15 & 0.013 \\ \hline Model 2 & \(-\)7.07 & \(-\)13.73 to \(-\)0.41 & 0.038 \\ \hline Model 3 & \(-\)7.34 & \(-\)14.00 to \(-\)0.68 & 0.031 \\ \hline Model 4 & \(-\)7.39 & \(-\)14.05 to \(-\)0.72 & 0.030 \\ \hline hsa-miRR-novel-chr5\_67265 & & & \\ \hline Model 1 & \(-\)2.87 & \(-\)4.59 to \(-\)1.15 & 0.001 \\ \hline Model 2 & \(-\)1.62 & \(-\)3.07 to \(-\)0.17 & 0.029 \\ \hline Model 3 & \(-\)1.35 & \(-\)2.81 to 0.12 & 0.071 \\ \hline Model 4 & \(-\)1.40 & \(-\)2.88 to 0.07 & 0.062 \\ \hline hsa-miRR-novel-chr13\_13519 & & & \\ \hline Model 1 & \(-\)4.05 & \(-\)6.70 to \(-\)1.41 & 0.003 \\ \hline Model 2 & \(-\)2.19 & \(-\)4.42 to 0.00 & 0.050 \\ \hline Model 3 & \(-\)1.73 & \(-\)3.97 to 0.51 & 0.131 \\ \hline Model 4 & \(-\)1.76 & \(-\)4.02 to 0.49 & 0.126 \\ \hline \hline \end{tabular}
\begin{tabular}{c c c c} \hline \hline  & **OR** & **95\% CI** & \(p\)**-Value** \\ \hline hsa-miRR-novel-chr1\_36178 & & & \\ \hline Model 1 & 1.20 & 1.09 to 1.32 & \(<\)0.0001 \\ \hline Model 2 & 1.22 & 1.10 to 1.35 & \(<\)0.0001 \\ \hline Model 3 & 1.23 & 1.10 to 1.38 & \(<\)0.0001 \\ \hline Model 4 & 1.22 & 1.10 to 1.37 & \(<\)0.0001 \\ \hline hsa-miRR-novel-chr15\_18383 & & & \\ \hline Model 1 & 1.33 & 1.02 to 1.74 & 0.038 \\ \hline Model 2 & 1.39 & 1.06 to 1.83 & 0.019 \\ \hline Model 3 & 1.46 & 1.11 to 1.93 & 0.007 \\ \hline Model 4 & 1.44 & 1.09 to 1.89 & 0.009 \\ \hline hsa-miRR-novel-chr2\_55842 & & & \\ \hline Model 1 & 1.70 & 1.40 to 2.07 & \(<\)0.0001 \\ \hline Model 2 & 1.66 & 1.36 to 2.04 & \(<\)0.0001 \\ \hline Model 3 & 1.65 & 1.34 to 2.04 & \(<\)0.0001 \\ \hline Model 4 & 1.65 & 1.33 to 2.05 & \(<\)0.0001 \\ \hline hsa-miRR-novel-chr7\_76196 & & & \\ \hline Model 1 & 4.40 & 2.37 to 8.19 & \(<\)0.0001 \\ \hline Model 2 & 4.34 & 2.27 to 8.30 & \(<\)0.0001 \\ \hline Model 3 & 4.64 & 2.37 to 9.08 & \(<\)0.0001 \\ \hline Model 4 & 4.89 & 2.48 to 9.64 & \(<\)0.0001 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Logistic regression analysis of blood miRNAs for prediction of CKD.
# Multi-Layer Problems and Solutions in VANETs: A Review
## 2 Multi-Layer Challenges in VANETs
### Multi-Layer Challenges
#### 2.1.3 Routing Layer  
Routing plays a very important role in the overall performance of any network. The optimum routing protocol sends the data through a series of hops using the shortest and less congested route. In VANETs the condition of the network has to be taken into consideration before forwarding the data as nodes join and leave the network frequently, the topology is unpredictable, and sometimes dedicated path is not available. The conventional routing algorithms does not suit the vehicular communication; thus it imposes the limitation on the performance of the network. The protocol should be capable of performing localized operations where routing decisions are purely based on information available in its vicinity. It eliminates the need for the node to know the entire topology of the network and thus it reduces the control overhead [PERSON] et al. (2016). The routing layer challenges include the:  
* Route data management.  
Figure 2: (**a**) The Hidden and (**b**) Exposed Terminal Problem.  
* Portioning of the network.
* Variable density of the network.
* An ideal routing protocol for VANETs should have the following features:
* Neighborhood Discovery.
* Data Forwarding Capability.
* Geographical Information.
* Predict the future position of vehicles.
* Consider the uneven vehicular density.
# Analyzing Meta-Heuristic Algorithms for Task Scheduling in a Fog-Based IoT Application
## 2 Related Work
### Traditional Algorithms  
In a FCFS scheduling algorithm, when a new task arrives, it is placed at the end of the queue. The first task at the beginning of the queue always runs first. This method has easy implementation. The round-robin (RR) method is based on the FCFS method for scheduling tasks so that resources are allocated to tasks for fixed periods. The advantage of this approach is load-balancing [7].  
In a Min-Min scheduling algorithm, the smallest job out of available tasks is selected; then, it takes a resource the minimum possible time to finish. This method increases makespan. The Max-Min method selects the longest work among existing tasks and assigns it to the fastest machines to run. In this method, smaller tasks must wait for a longer period, and this increases the makespan. However, this method has a better makespan than its peers' algorithms [9].  
In a priority scheduling algorithm, tasks are classified based on their priority. These priorities are considered using QoS parameters. Then, resources with the best completion time are assigned to these tasks [17].  
In [12], FCFS, concurrent, and DP scheduling methods are used for task scheduling in the iFogsim environment. They provided two case studies, EEGTBG and VSOT, then analyzed the results by delay, total network usage, and the number of application modules based on the number of users. The results show that minimum delay is obtained by the DP method, and minimum network usage occurs with the concurrent method.
# Image Watermarking between Conventional and Learning-Based Techniques: A Literature Review
###### Abstract  
Currently, most transactions and exchanges are conducted through the Internet thanks to technological tools, running the risk of the falsification and distortion of information. This is due to the massive demand for the virtual world and its easy access to anyone. Image watermarking has recently emerged as one of the most important areas for protecting content and enhancing durability and resistance to these kinds of attacks. However, there is currently no integrated technology able to repel all possible kinds of attacks; the main objective of each technology remains limited to specific types of applications, meaning there are multiple opportunities to contribute to the development of this field. Recently, the image watermarking field has gained significant benefits from the sudden popularity of deep learning and its outstanding success in the field of information security. Thus, in this article, we will describe the bridge by which the watermarking field has evolved from traditional technology to intelligent technologies based on deep learning.  
image watermarking; deep learning; digital images; copyright protection; information security; visual imperceptibility; robustness; review
## 2 Measurement Set-Up and Procedure
Loading resistance was selected to ensure a sufficiently long discharging time to record very low frequency components, even with fractions of Hz. The specimen was fully charged before our experiment. The charging time was long enough to secure charging of both capacities C\({}_{\text{H}}\) and C\({}_{\text{D}}\) (Figure 1). Otherwise, after disconnecting the power supply unit, the charges collected in the capacitance C\({}_{\text{H}}\) will flow to the second capacity C\({}_{\text{D}}\), which may induce false measurement results. Figure 3: Block diagram of the measurement set-up applied for voltage noise time series recording. In our experiment, a 2 h period of charging using the constant voltage method preceded by charging with a constant current to a given voltage (1.5 V) was applied. The recorded discharging current is processed to remove a current of exponential decay and expose additive noise. Subsequently, the signal is divided into segments for which the power spectral densities are determined and then averaged to reduce random error. When the voltage across the supercapacitor's terminals reaches a value close to 0 V, the capacitor is reconnected to the power supply. Afterwards, the charging cycle for rated voltage (U\({}_{\mathrm{c}}\)) and discharge to \(\sim\) 0 V (approximately 10 mV) with constant charging/discharging current is repeated five times. The capacitance \(C\) and the equivalent series resistance \(R_{ESR}\) are determined based on the voltage waveform recorded during the fifth discharge. This repeated operation of charging/discharging stabilizes the specimen to ensure repeatable measurement results.
## 3 Design
### Transactions  
In the IoT network, the information may flow from the sensor to the control center through the blockchain network, and the control center can also send messages to one or more sensors. The information flow in both directions is achieved through the employment of the lightweight communication protocol designed specifically for the IoT network, such as MQTT and CoAP [28]. In the IoT network integrated with blockchain, the smallest unit of communication is called a transaction. The tellers are in charge of packing a new block of transactions, and all the nodes help to spread the transactions just like in the regular blockchain network.  
If the control center requests data from one or more target sensors, this transaction will be directly sent to the blockchain network. Each node in the blockchain network will check if the target sensors are connected to itself; if so, the node will send the transaction to the connected sensors.  
If the transaction is initiated from a sensor, no matter if it is a publish message or response message, the transaction will be sent to the connected edge nodes. This transaction will be split into \(n\) pieces (\(n\) is the number of tellers), and then spread through the blockchain network. These split \(n\) pieces will be encrypted using the public key of tellers. Therefore,except for the node that splits the transaction and the control center, no other node can merge these encrypted pieces and fetch the sensitive information. Because the control center is aware of the tellers' private key, the control center is able to recover the original information sent by the sensors. In this way, even if one or some nodes are controlled by a malicious attacker, the attacker still cannot break the protection of the information; thus, the privacy is preserved.
## 6 Conclusions  
In this work, we first define the network system, energy expenditure and replenished model, data aggregation scheme, and elaborate on the packet delivery process. The unique characteristics of R-WSNs pose a big challenge for packet's transmission. On the one hand, data collected by many sensors is based on common phenomena, and hence there is a high probability that this data has some redundancy. On the other hand, due to energy being replenished continually and limited energy storage capacity, it might not always be beneficial to conserve energy when a network can harvest excessive energy from the environment. Therefore, reasonable energy usage can improve data collection rate and sensor usage efficiency.  
In order to improve data generation rate, we propose an algorithm to compute an upper data generation rate based on data fusion that maximizes it as an optimization problem for a network. First and foremost, we formulate it as a linear programming problem subject to the flow and energy conservation constraints. On top of that, a dual problem by introducing Lagrange multipliers is constructed. Last but not least, a subgradient algorithm is used to solve it in a distributed manner. Through extensive simulation and experiments, we demonstrate that our algorithm is efficient to maximize data collection rate in rechargeable wireless sensor networks.
# Highly Adaptive Reconfigurable Receiver Front-End for 5G and Satellite Applications
## 5 HARRF LNA Design
### K/Ka-Band LNA Design Procedure
#### 5.2.3 Comparison with State-of-the-art K/Ka-Band LNAs  
Table 2 lists state-of-the-art LNAs that operate in the K/Ka frequency band. Processes vary from CMOS, SiGe, GaAs to InP, designed for SATCOM or emerging 5G applications. Each design is characterised by its operating frequency, gain, noise figure, and power consumption. The design in [18] targets both 5G and SATCOM applications in the 23 GHz to 28 GHz band using a 0.15 \(\upmu\)m InGaAs pHEMT technology.  
Figure 14: Input and Output reflection coefficient of the K/Ka-band MMIC LNA circuit.  
Figure 13: Schematic of K/Ka-Band LNA ([18]).  
Figure 16: Noise Figure of the K/Ka-band MMIC LNA circuit.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Ref.** & **Process** & **Application** & **Freq. (GHz)** & **Gain (dB)** & **Noise (dB)** & **Power (mW)** \\ \hline
[13] & 0.4 μm CMOS SOI & SATCOM & 20–22 & 3-17 & 8 & 2.82 \\
[26] & 0.25 μm SiGe & SATCOM & 19.7–21 & 0 & 5 & 0.24 \\
[27] & 0.4 μm CMOS & 5G & 26.5–29.5 & 0 & 4.2 & 0.9 \\
[28] & 0.28 μm CMOS & 5G & 25.8–28 & 30–69 & 6.7 & 0.4 \\
[29] & 0.13 μm SiGe & 5G & 27.5–29 & 30 & 3.7 & 3.3 \\
[30] & 0.65 μm CMOS & SATCOM & 17–21 & 16–34 & 5.4 & 0.93 \\
[31] & 0.15 μm AlGaAs-InGaAs pHEMT & 5G & 26-33 & 22.8 & 1.6 & - \\ \hline \end{tabular}
\end{table}
Table 2: State-of-the-art LNAs operating at the target K/Ka frequency band.  
Figure 15: Gain of the K/Ka-band MMIC LNA circuit.
# Inter-UAV Routing Scheme Testbeds
## 2 Ad Hoc Routing
### Testbed-Friendly Ad Hoc Routing Protocols
#### 2.1.4 Batman  
The Better Approach To Mobile Ad hoc Networking (BATMAN) protocol is a decentralized routing protocol whose main selling point is the equal distribution of the optimal route information. This approach eliminates the need to broadcast possible topology changes to the entire network, effectively eliminating overhead. Nodes do not maintain full source-destination route information and instead only know their next hop. Route optimization is implemented in a hop-by-hop approach. Packets are routed to their destination individually, via constantly recomputed routes (that applies to packets comprising a single message as well). The BATMAN-adv variant of this protocol enables layer 2 routing (similarly to HWMP), in contrast to the classical layer-3 routing scheme normally implemented by BATMAN [49]. BATMAN-adv is currently implemented as a kernel driver and improves CPU performance by minimizing the required machine cycles [50].  
Periodically, Originator Messages (OGM) are broadcasted to update neighboring node information; they contain the following information [51]:  
* Originator (i.e., source) address
* Destination address
* Link flag
* Time To Live (TTL)  
The information of the destination address is updated every time the OGM packet is rebroadcasted. Information regarding reachable nodes is stored in an originator table, carried by each node. BATMAN uses the Transmission Quality metric (TQ) to compare link quality between possible next hops and perform route selection [51]. Authors in [26] used BATMAN in a physical Raspberry Pi-based network. They propose a testbed framework for this protocol and additionally tested video content transmission in a BATMAN-enabled ad hoc network.  
Table 2 contains and summarizes the main characteristics of the testable routing protocols which were analyzed in this subsection: OLSR Section 2.1.1, AODV Section 2.1.2, HWMP Section 2.1.3 and BATMAN Section 2.1.4.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Routing Protocol** & **Proactive** & **Reactive** & **Open-Source** &
\begin{tabular}{c} **Kernel** \\ **Implementation** \\ \end{tabular} \\ \hline OLSR & \(\checkmark\) & \(\checkmark\) & \(\checkmark\) & OLSRd \\ \hline AODV & \(\checkmark\) & \(\checkmark\) & \(\checkmark\) & AODV-UU \\ \hline HWMP & \(\checkmark\) & \(\checkmark\) & \(\checkmark\) & HWMP \\ \hline BATMAN & \(\checkmark\) & \(\checkmark\) & \(\checkmark\) & BATMAN-adv \\ \hline \hline \end{tabular}
\end{table}
Table 2: Routing scheme summary table.
# A Review of Neuro-ML Breakthroughs in Addressing Neurological Disorders
## 4 Results
### Review of ML on AD
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Subject** & **Metric** & **Value** & **Reference** \\ \hline \multirow{3}{*}{Multi-stage diagnosis of AD} & ROC AUC (Clinical) & 98.9\% (CogN) & 92.7\% (MCI) & 90.7\% ( Dementia) & \\  & ROC AUC (Genomic) & 76.3\% (CogN) & 76.1\% (MCI) & 70.6\% ( Dementia) & \\  & F\({}_{1}\)-Score (Clinical) & 97.1\% (CogN) & 93.9\% (MCI) & 88.6\% ( Dementia) & \\  & F\({}_{1}\)-Score (Genomic) & 71\% (CogN) & 77\% (MCI) & 53\% ( Dementia) & \\ \hline Differences in progression & [PERSON]’s c-index & 0.87 (M-RSF) & 0.79 (F-RSF) & - & [148] \\  & [PERSON]’s c-index & 0.87 (M-RSF) & 0.79 (F-RSF) & - & [148] \\ \hline \multirow{3}{*}{ML for AD diagnosis using MRI} & Accuracy & 97.46\% & - & - & \\  & F\({}_{1}\)-Score & 93.19\% & - & - & [149] \\  & Recall & 95.79\% & - & - & \\  & Precision & 94.62\% & - & - & \\ \hline \multirow{3}{*}{Detecting AD using PET and MRI images} & Accuracy (Binary) & 99\% & - & - & \\  & Accuracy (AD vs. MCI) & 91\% & - & - & [150] \\  & Accuracy (Multi-class) & 96\% & - & - & \\ \hline \multirow{3}{*}{CAD for detecting AD stages} & Accuracy & 99.21\% & - & - & \\  & Sensitivity & 99.69\% & - & - & [127] \\  & F\({}_{1}\)-Score & 99.61\% & - & - & \\ \hline \multirow{3}{*}{Evaluating dual-phase FBB vs. delay-phase FBB} & F\({}_{1}\)-Score (SUVR composite) & 78.06\% (RF) & - & - & \\  & AUC (SUVR composite) & 0.8724 (RF) & - & - & [151] \\ \cline{1-1}  & F\({}_{1}\)-Score (SUVR regional) & 78.54\% (RF) & - & - & \\ \cline{1-1}  & AUC (SUVR regional) & 0.8456 (RF) & - & - & \\ \hline \multirow{3}{*}{Ontology for AD diagnosis} & Accuracy (MLP) & 92.12\% & - & - & [152] \\  & Accuracy (CNN) & 94.61\% & - & - & \\ \hline \multirow{3}{*}{Untargeted metabolomic profiling for MCI dementia conversion} & Accuracy (3-class) & 73.50\% & - & - & \\  & Accuracy (P-MCI vs. & 80.30\% & - & - & [154] \\ \cline{1-1}  & Accuracy (63 features) & 90.2\% (RF) & 89.6\% (MLP) & 90.5\% (CNN) & \\ \cline{1-1}  & \multirow{3}{*}{Accuracy (22 features)} & \((-\)3.8\%) for RF vs. MLP & - & & [155] \\ \cline{1-1}  & & \((-\)6.8\%) and CNN & - & - & \\ \cline{1-1}  & & \((-\)4.5\%) & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparative performance metrics of the discussed literature related to AD. reflect the method's performance concerning the metrics analyzed in the paper. Weakness (1-5) reflect the severity of the technique's limitations or weaknesses.
## 3 Results and Discussion
### Adhesion Strength  
Figure 9 presents SEM micrographs of the coated carbide specimens. Following the Daimler-Benz adhesion test, the CrN/CrCN/DLC coating falls in the class HF4 group testifying acceptable adhesion to the substrate. Delamination is seen in Figure 9a. It could be the result of intrinsic stress owing to the cumulative impact of failures that appeared internally in the film during the deposition process [32]. Furthermore, few radial micro cracks were present around the imprint.  
CrN/AlTiN and CrN/CrCN coatings are classified in the HF1 adhesion level denoting high adhesion strength. A few radial micro cracks were observed in the vicinity of the imprints of both coatings as a typical failure, demonstrating the relaxation of stress at boundaries between layers (Figure 9b,c). The cracks most likely stemmed from tensile stresses associated with the material piling up during the test, which relates to hard and brittle coatings [33].  
CrCN/TiSiCN coating is in the HF3 category indicating a good adhesion strength. Radial micro cracks around the indentation and delamination are observed in Figure 9d.  
Figure 9: SEM micrographs of Daimler–Benz adhesion tests carried out on coated carbide substrates (a) CrN/CrCN/DLC, (b) CrN/AlTiN, (c) CrN/CrCN, and (d) CrCN/TiSiCN.  
SEM micrographs at higher magnification of the specimens used to carry out the Daimler-Benz adhesion tests clearly show the presence of radial micro cracks around the imprint of the tested coatings (Figure 10).
# Millimeter-Wave Smart Antenna Solutions for URLLC in Industry 4.0 and Beyond
## 5 Research Opportunities and Future Directions
### Terahertz Communication  
Terahertz (THz) communication is considered an enabling technology to envisage beyond-5G and 6G systems [165; 166; 167]. Industry 4.0 and beyond can also benefit from THz communication for future mission-critical applications with the high demand for massive URLLC [168; 169; 170; 171; 172]. However, more difficult propagation conditions exist at THz frequencies, such as increased path loss, higher penetration loss, and more severe shadowing. Besides other challenges such as standardization, design of THz sources, THz integrated circuits, and advanced THz antenna fabrication techniques [173; 174; 175; 176].  
THz antennas are the key devices for THz communication because of their tiny size, wide frequency bandwidth, and high data rate [177; 178]. A plethora of THz antenna designs are reported in the literature for THz communication [177; 178; 179; 180; 181; 182; 183; 184; 185; 186; 187], to list but a few. Graphene is extensively used in THz antennas due to its exceptional optical properties [188; 181]. THz communication is believed to be an emerging and enabling technology in future Industry 4.0 and beyond to provide extremely high data rates, with open research challenges [168].
## 5 Experimental Results
### Comparison with Other Representation Learning Methods
AFF_CGE demonstrates relatively stable performance with F1-scores between 0.903 and 0.929. When using only BS-F as input, GTN, GCN-ETA, and AFF_CGE achieve maximum improvements of 0.227, 0.228, and 0.228, respectively, compared to BS-F, indicating that these methods effectively extract deep semantics from base features and exhibit strong feature extraction capabilities. Notably, AFF_CGE performs best and most consistently across most classifiers (among the five classifiers tested, it performed best in four of them), highlighting its significant advantage in extracting features from encrypted malicious traffic and providing robust support for its effective identification.
## 4 Results
### Application to Real Biological Data
#### 4.2.4 Comparison with Spectral-Clustering  
In order to compare our CORE-clustering strategy with a standard clustering approach, we finally estimated representative variables in the Yeast dataset as the center of clusters estimated using spectral clustering. The standard version of the spectral clustering available in \(R\) was used. Its main parameter is the number of seeds \(\eta\) used in the \(\mathbf{k}\)-means part of the spectral clustering. When using \(\eta\) seeds, an amount of 1 to \(\eta\) clusters (with more than one variable) are estimated using \(\mathbf{k}\)-means and all variables are contained in a cluster. In order to fairly compare the spectral clustering and the CORE-clustering approaches, we then clusterized the variables of the Yeast dataset using \(\eta=\{5,30,50,70,110\}\). Note that we only tested an \(\eta\) higher than 77, due to the fact that \(n=77\) observations are known. We tested \(\eta=110\) to evaluate the spectral clustering behaviour with \(\eta\) slightly higher than \(n\).  
An amount of \(\{3,3,6\}\) large clusters were obtained for \(\eta=\{50,70,110\}\), respectively. For \(\eta=\{5,30\}\), only a single cluster gathering almost all variables was also obtained. The average coherence of the estimated clusters was 0.44 for a standard deviation of 0.06. The highest coherence was 0.63, which is clearly lower than the considered threshold of \(\xi=0.75\) that we used with the CORE-clustering. This makes ambiguous the interpretation of the role played by the representative variables.  
Finally, it is worth mentioning that all representative variables (genes) obtained using Algorithm 2 have a known physiological function and only two variables out of eleven have an unknown function by using Algorithm 3. For the spectral clustering with \(\eta=110\), four selected variables out of six have known functions. For \(\eta=70\), three variables with unknown functions were selected. For \(\eta=50\), two variables out of three with known functions were selected. For \(\eta=5\) and \(\eta=30\), the single selected variable was the center of all variables with respect to the center definition in Section 3.3, and turns out to have a known function. It therefore appears in this experiment that the representative variables obtained using CORE-clustering are more interpretable than those estimated using spectral clustering.