esa-sceva/satcom-chunk-collection huggingface dataset:
# A Comprehensive Review of In-Body Biomedical Antennas: Design, Challenges and Applications
## 2 Design Specifications
### Wireless Link Consideration  
Figure 3 shows a generalized wireless communication link between an external device and an IBBD with a transceiver system and an antenna on each side.  
The wireless communication link can be classified as near field and far field. The near field technique includes inductive coupling [14]. It was the technology integrated into the first wireless implants at an operation frequency of 20 MHz or lower [4]. This technology established the use of inductors within IBBDs and external devices, which were located in close proximity to initiate wireless communication through coupling. The link design strategies for inductively coupled IBBDs are explained broadly in Refs. [14; 44]. In far field, the link power budget can be written as [29]  
\[\begin{array}{l}\quad Link\;margin\;(dB)=Link\frac{C}{N_{0}}-\;Required\; \frac{C}{N_{0}}\\ =P_{t}+G_{t}-L_{f}-L_{imp}+G_{r}-N_{0}-\frac{E_{r}}{N_{0}}-10 log_{10}B_{r}\\ +G_{c}-G_{d}\end{array} \tag{1}\]  
Figure 3: Wireless communication link between external device and IBBD.  
where \(C/N_{0}\) is the ratio of the carrier power and noise density; \(P_{t}\), \(G_{t}\), \(L_{f}\), \(G_{r}\), \(B_{r}\), \(G_{c}\), \(G_{d}\) and \(E_{b}/N_{0}\) are the transmit power, transmit antenna gain, path loss (free space), receiver antenna gain, receiver bit rate, receiver coding gain, receiver deterioration and the ratio of energy per bit and noise density, respectively. The path loss can be calculated according to the free-space reduction in signal strength with the distance \(d\) between the transmitter and receiver as [29]  
\[L_{f}\left(dB\right)=20 log\left(\frac{4\pi d}{\lambda}\right) \tag{2}\]  
Furthermore, the impedance mismatch loss can be calculated as  
\[L_{imp}\left(dB\right)=-10 log\left(1-\left|\Gamma\right|^{2}\right) \tag{3}\]  
where \(\Gamma\) is the appropriate reflection coefficient. The loss in human body is not considered in Equation (1), and this will be explained further in Section 4, where different challenges for in-body antenna design are described.
## 4 Simulation Results
### Financial Impacts for Power Producers and the Retailer
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Pricing Scheme** & **Uniform** & \multirow{2}{*}{**2-Zonal (SMP)**} & \multirow{2}{*}{**2-Zonal (ZMP)**} & **5-Zonal (ATC, ZMP)** & **5-Zonal (FBMC, ZMP)** \\ \cline{2-5}  & & Power Purchase Cost & 93,810 & 87,279 & 79,962 & 79,984 & 80,313 \\ \hline \multirow{4}{*}{Power Producers} & Generation Cost & 39,317 & 39,317 & 39,317 & 39,317 & 39,271 \\ \cline{2-5}  & Energy Revenues & 93,905 & 85,562 & 78,425 & 78,446 & 79,088 \\ \cline{1-1} \cline{2-5}  & Make-Whole Payments & 1592 & 1947 & 2805 & 2805 & 2853 \\ \cline{1-1} \cline{2-5}  & Profit & 56,179 & 48,191 & 41,913 & 41,934 & 42,670 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Comparative financial impacts of uniform and zonal pricing. were considered, the opposite trend in the generation costs would likely be observed. In other words, the general consensus is that when the total system costs include both the generation costs of the wholesale electricity market and the redispatch costs of the balancing market, nodal pricing typically yields the lowest total costs [1; 27; 2]. The results presented in Tables 9 and 10 differ from this consensus because we excluded the balancing market from our analysis. This limitation of the study is discussed in greater detail in the conclusion section.
# IEEE 802.11 ah: A Technology to Face the IoT Challenge
## 1 Introduction
Shipments of IEEE 802.11 devices reached 12 billion just at the beginning of 2016, and will reach 15 billion by the end of 2016, according to current predictions (information extracted from Wi-Fi Alliance). Current literature on IEEE 802.11 ah, however, does not provide enough evidence to support the suitability of this technology in an IoT scenario. In this regard, this paper shows how IEEE 802.11 ah can cover the requirements of the most common IoT applications. \begin{table} \begin{tabular}{l c c c c c c c} \hline \hline \multirow{2}{*}{**Feature**} & **IEEE 802.11 (n/ac)** & **IEEE 802.11 ah** & **ZigBew/802.15.4e** & **BLE** & **3 GPP MTC** & \multicolumn{2}{c}{**LLPWAN**} \\ \cline{4-7}  & & & & & & **LoRaWAN** & **SigFox** \\ \hline \multirow{2}{*}{Frequency band (GHz)} & Unlicensed & \multirow{2}{*}{Unlicensed 900 MHz} & Unlicensed 868/915 MHz & Unlicensed & Unlicensed & Unlicensed & Unlicensed \\  & 2.4 5 GHz & 2.4 GHz & 2.4 GHz & \(<\)5 GHz & 867-928 MHz & 868-902 MHz \\ \hline Data Rate & 6.5–6933 Mpps & 150 kbps–346 Mbps & \(<\)280 kbps & \(<\)1 Mbps & \(<\)1 Mbps & \(<\)25 kbps & \(<\)1 kbps \\ Coverage range & \(<\)200 m & \(<\)1.5 km & \(<\)100 m & \(<\)50 m & \(<\)100 km & \(<\)20 km & \(<\)60 km \\ Power consumption & Medium & Low & Low & Low & Low & Low & Low \\ \hline Number of devices supported & 2007 & 8000 & 65,000 & Unlimited * & \(>\)100,000 & \(>\)100,000 & \(>\)1,000,000 \\ \hline \hline \end{tabular}
# Energy Optimization in Cluster-Based Routing Protocols for Large-Area Wireless Sensor Networks
## 1 Introduction
Broken links are detected by route maintenance and substituted by some new paths in the existing route. The main drawbacks of this protocol are message overheads and complexity. In P-LEACH [15], a mobile base station (BS) is considered instead of a fixed BS. It uses a cluster-based prediction by activating a small number of nodes during the tracking of the mobile BS in the field, which is divided into three regions: partition cluster, communication quadrangle, and four-partition-cluster structure. Aside from data routing protocol issues, the authors of [16] presented a survey on cluster-based group key agreement (GKA) protocols for WSN security issues, and assessed and measured the efficiency of each GKA protocol in terms of its energy consumption. Cluster-based routing protocols divide the sensor nodes into an average of \(k\) clusters. In conventional WSN, we used the parameter \(k\) as a sub-optimized fixed value by rule of thumb, which worked well enough with small-area sensor networks. In large-area WSNs, \(k\) needs to be optimized with increased number of sensor nodes. Decentralized cluster-based routing protocols use control messages for cluster head advertisement, join request, message transmission scheduling, etc. In LEACH algorithm [17] and most of its variants, nodes send control messages with maximum power to reach the entire network to ensure that the messages can be received by nodes farthest away in the sensor field. Using this non-optimized max-distance transmission range of \(R\) for the control messages, together with another sub-optimized fixed value of \(k\), is one of the main causes of limited improvements in lifespan of large area sensor network in IoT [18; 19]. Referring to a survey paper over a hundred of cluster-based routing protocols [20], we found some approaches [21; 22] to optimization of \(k\), without compromising decentralized-clustering features. In [21], the authors investigated the optimization of \(k\) using a genetic algorithm, in which, however, the improvement significantly degraded by increasing the size of sensor field from 50 m to 100 m, which are still not big enough to be applied in IoT. In [22], uneven probabilities are applied to the election of CH nodes, while using suboptimal \(k\) determined by rule of thumb. In general, we could hardly find cluster-based routing protocols with optimization of \(R\), the transmission range for control messages, or with joint optimization of \((k,R)\). In this paper, we investigate a joint optimization of \((k,R)\) by in-depth analyses of energy consumption during clustering processes and data transmission in sensor nodes. Table 1 is a list of symbols and notations used in this paper.
# Smart Agriculture Applications Using Deep Learning Technologies: A Survey
## 6 Discussion  
The agriculture sector needs to be modernized with state-of-the-art technologies to feed the rapidly increasing world population with bio food. Given the increasing environmental problems that negatively affect this sector, it was necessary to move to the use of modern technologies to meet these challenges and issues with high efficiency. Smart agriculture is a vast field that includes a variety of topics such as sensor development, parameter monitoring, data collection, network convergence and maintenance, sensor nodes cluster formation, cluster head selection, data compression, and aggregation, security and integrity, design and development of the expert system, artificial intelligence-based decision making and many more.  
The development of artificial intelligence and deep learning in the past two decades has dramatically increased the number of projects in the field of agriculture. The analysis has shown that deep learning techniques offer superior performance in most related work and yield better results than traditional methods in this sector.  
Some critical factors that were ignored can exert considerable influence on the decisions made by any AI or deep learning algorithm. We observed that crop, soil, environment, and pest-related information is collected using field sensors before applying a deep learning algorithm to obtain the next course of action; however, other parameters, such as weather forecasts, crop standard procedures, historical crop data, farmer input, and government policies, are not taken into account while decisions are made.  
\begin{table}
\begin{tabular}{c c c} \hline
**No** & **Performance Metric** & **Description** \\ \hline
1 & Accuracy & This is the \% of correct classifications. \\ \hline
2 & Precision & Precision is defined as the fraction of relevant examples (true positives) among all of the examples that were predicted to belong to a certain class. \\ \hline
3 & F1 score & F-measure provides a single score that balances both the concerns of precision and recall in one number. \\ \hline
4 & R’2 & The determination coefficient is a statistical measure representing the proportion of the variance for a dependent variable explained by an independent variable. \\ \hline
5 & Root Mean Square Error (RMSE) & It is a standard deviation of the errors that occur when a prediction is made on a dataset. \\ \hline \end{tabular}
\end{table}
Table 2: Performance metrics used in related work.  
Figure 8: Data augmentation.
## 4 Materials and Methods
### Extraction and Determination of Volatile Compounds  
In _Anthurium_, floral scents are mainly emitted at stage S2. Thus, the spadices at stage S2 were harvested for volatile constituent analysis, according to [PERSON] et al. [39]. The spadix was enclosed in a 500 milliliter (mL) glass bottle with the addition of 1.728 micrograms (ug) ethyl caprate, which served as an internal standard. After waiting 30 min to achieve equilibrium, a polydimethylsiloxane fiber (PDMS, with 50/30 micrometer (um) divinylbenzene/Carboxen) fiber (Supelco) was used to collect volatiles. Then, the collected volatiles were detected by GC-MS using an Agilent 7890A GC and Agilent 5975C MSD. The instrument was equipped with an Agilent HP-5 MS capillary column (30 m \(\times\) 0.25 mm) and helium was used as a carrier gas at a constant flow of 1.0 mL/min. The oven temperature was initially maintained at 45 \({}^{\circ}\)C for 2 min, followed by an increase of 5 \({}^{\circ}\)C/min until it was finally maintained at 250 \({}^{\circ}\)C for 5 min. Identification of individual compounds was performed by the comparison of mass spectra and retention times with authentic standards, or with the NIST 08 mass spectra library. Quantification was based on peak areas and the quantity of internal standard using the Agilent ChemStation Data Analysis Application. The relative content of aroma components was calculated as follows: the mass of compound (ug-gFW\({}^{-1}\)-h\({}^{-1}\)) = mass of internal standard \(\times\) area under the peak of a compound/area under peak of internal standard/fresh weight of sample.
## 4 Results and Discussion
### Underground Implementation of a Digital-Twin-Based Spatial Object Extraction Algorithm
#### 4.1.4 Supplementing the Algorithm via Octree Creation  
To extract object changes using an octree, several steps must be undertaken. First, the point cloud for the bounding box is computed and the root node (the initial node) is established. Subsequently, the octree is segmented, dividing the nodes into eight smaller nodes. Each node is characterized by its geometry and address, and its form is determined with the coordinates of the node center. Each node within the octree structure has a unique address, reflecting the connections between neighbor nodes and the pathway from the present node to its ancestor node. Defining appropriate terminal criteria for octrees is essential. In this study, the minimum node size and minimum point count were established as the terminal criteria. Moreover, the spatial division was assumed to cease if the node met the minimum point threshold, even if the minimum node size criterion was yet unmet.  
Figure 4: Point spatial object extraction process.  
The entire iterative procedure formed the octree, and a list of points was stored in each occupied leaf.
## 3 Application to Experimental Data  
To demonstrate the fusion technique's performance under realistic conditions, a test specimen containing 15 surface flaws was inspected using three different NDT methods. In this section, we describe the specimen, the individual data collection and processing as well as the application of our fusion algorithm. Finally, we quantitatively evaluate the effect of various conditions on the fusion result, and compare fused detection against single-sensor detection. To further corroborate the effectiveness of our fusion approach, experiments are replicated using a second specimen. Results for this test piece are provided in the Supplementary Material to this article.
## 2 Materials and Methods
### Nonlinear Model Predictive Control
The base policy is obtained by solving the optimal control problem for the entire route with the speed limits available at the beginning of the trip. The full route optimal control problem is the same as described by Equations (10) and (11) but with \(n=0\) and \(N_{h}=N\), where \(N\) is the total number of steps for the full route. The terminal cost \(G(x_{N})\) is such that the SOC at the end of the trip is equal to the SOC at the start of the trip, i.e.,  
\[G(x_{N})= \begin{cases}0&SOC_{N}=SOC_{0}\\ inf&otherwise\end{cases}  \tag{12}\]  
where \(inf\) could be a very large number as compared to the expected value function [20]. The full route optimal control problem is solved using DP to obtain the optimal cost-to-go or value function \(G^{*}(x,i)\) for all \(i=0,1,...,N\). The terminal cost for a horizon \(n\) to \(n+N_{h}\) is then \(G(x_{n+N_{h}})=G^{*}(\cdot,n+N_{h})\). In the rollout algorithm, at each step \(n\), the optimal control problem given by Equations (10) and (11) is solved using DP. The key difference between the base policy and the rollout is that the latter receives updated speed limits at every step \(n\). The rollout algorithm further provides many benefits including a closed-loop optimal policy that matches the full-route optimization in the case where no disturbances are experienced, and it eliminates the need to define terminal constraints on the SOC while ensuring approximately charge-sustaining behavior over the entirety of the trip [21]. If SOC constraints are instead defined for every horizon length, the result is a conservative strategy which attempts to drive the SOC back to the initial value for each segment. This limits the SOC range and could potentially decrease optimality since the vehicle will not be able to take advantage of the entire usable battery range.
## 3 Mobility-Aware Duty Cycling
### Network Topology  
With regard to network topology, we have taken two approaches. Firstly, a one-hop grid formation is used. This in order that results could be observed when controlled location of static nodes was in effect. As such, it is easier to observe the effect that communication with the MSN has on the static nodes in the network, with both interference range and distance from the path of the sink node reasonably consistent. Figure 1 shows the network layout, with the start point of the MSN and the clockwise direction of travel.  
In addition, a more random topology has been deployed in order to ensure that MADCAL will also work in more diverse circumstances. This can be seen in Figure 2.  
As can be observed, depending on the interference range, certain nodes within the network will be within one-hop of the MSN. These are determined to be the aforementioned significant nodes, taking the place of hotspot nodes in a network where the sink node remains static, only now the significant  
Figure 1: Network topology—grid.  
nodes take it in turn to have the final responsibility for relaying data to the sink. As the ultimate aim is to reduce energy consumption and increase network lifetime, this benefit is negated if only certain nodes have channel access and thus, the ability to communicate with the sink node. Whilst other nodes wait for the channel to become clear before communication with the sink can commence. Therefore, we propose the development of a threshold of communication in order to ensure fair access to the MSN.
## 2 Linear and Nonlinear Methods
### Artificial Neural Network Approach
#### 2.2.1 BPNN  
The back-propagation neural network (BPNN), which is a type of multilayer feedforward neural network, has been widely applied in various fields of forecast and recognition [36]. As shown in Figure 1, BPNN includes three types of basic network layers, namely, the input layer, the hidden layer(s), and the output layer, and each layer has various numbers of nodes that aim to obtain a scalar result by computing the inner product of the input vector and weight vector through an activation function. The calculating process of each layer can be given as  
\[\mathbf{a}^{l}=\sigma\Big{(}w^{l}\mathbf{a}^{l-1}+\mathbf{b}^{l}\Big{)}, \tag{3}\]  
where \(\mathbf{a}^{l}\) represents the output vector of the \(l^{\text{th}}\) layer; \(w^{l}\) and \(\mathbf{b}^{l}\) are the connection weight vector from the \((l-1)^{\text{th}}\) layer to the \(l^{\text{th}}\) layer and the bias vector of the \(l^{\text{th}}\) layer, respectively; and \(\sigma\) is the nonlinear activation function, which is usually a rectified linear unit (ReLU) activation function or a sigmoid activation function.  
This artifical neural network can be trained via the gradient descent method that minimizes the loss function of the network to tune the weight vectors and threshold vectors of the joints between two layers. The most commonly used loss function is the squared error loss, which can be expressed as  
\[\Gamma(\mathbf{y^{o}},\hat{\mathbf{y}})=k\|\mathbf{y^{o}}-\hat{\mathbf{y}}\|_{2}^{2}, \tag{4}\]  
where \(\mathbf{y^{o}}\) and \(\hat{\mathbf{y}}\) represent the desired output and forecasting output, respectively; \(k\) is a constant weight and can be set as 1; and \(\left\lVert\cdot\right\rVert_{2}\) denotes an operator of \(L^{2}\) norm.
## 4 Experimental Results
### Convergence Characteristics  
In Figure 12, each algorithm's convergence curve is depicted for each model and indicates the average RMSE performance across 30 independent runs. As can be seen from Figure 12, it is evident that the ICO achieved a competitive or faster convergence rate than the other algorithms for the three PV models, demonstrating its capability to maintain a good balance between exploration and exploitation. It is worth noting that the convergence behavior of the CO seems better than that of the other conventional algorithms, such as DE, GA, PSO, WGO, SSA, TLBO, JAYA, and WSO.  
Figure 11: CPU time over 30 runs by different algorithms with \(n\) = 40 for SDM, DDM, and PVMM.
## 4 Results and Discussion
### Ablation Study Results of Transformer Models
#### 4.1.4 Ablation Study of DistilBERT  
Based on the results in Tables 8 and 9, the attention mechanism is identified as the most critical component in the DistilBERT architecture for FND, with its removal leading to the largest average performance drop of 0.1823 and a standard deviation of 0.2054. In contrast, residual connections appear to be the least impactful, showing a negligible average performance drop of just 0.0005 and a standard deviation of 0.0007, indicating their minimal contribution to predictive accuracy in this context. Dataset-specific analysis reveals that the baseline configuration performs best on both the FakeNewsNet and ISOT datasets with accuracies of 0.5250 and 0.9990, respectively, while the LIAR dataset achieves the highest accuracy of 0.5960 when the feedforward component is removed, suggesting some architectural redundancy or overfit mitigation in that setting. In general, the average baseline performance across the datasets is 0.7023, with a standard deviation of 0.2111, reaffirming the strong baseline capabilities of DistilBERT and highlighting the central role of attention mechanisms in maintaining its performance.  
\begin{table}
\begin{tabular}{l l c c} \hline
**Dataset** & **Configuration** & **Test Accuracy** & **Test F1** \\ \hline \multirow{8}{*}{ISOT} & baseline & 0.9997 & 0.9997 \\  & no\_disentangled\_attention & 0.9990 & 0.9990 \\  & no\_enhanced\_mask\_decoder & 0.9990 & 0.9990 \\  & no\_relative\_position & 0.9997 & 0.9997 \\  & no\_feedforward & 0.9987 & 0.9987 \\  & no\_residual\_connections & 0.9990 & 0.9990 \\  & no\_layer\_normalization & 0.9990 & 0.9990 \\  & no\_dropout & 0.9990 & 0.9990 \\ \hline \multirow{8}{*}{LIAR} & baseline & 0.5757 & 0.5764 \\  & no\_disentangled\_attention & 0.5997 & 0.5764 \\  & no\_enhanced\_mask\_decoder & 0.6023 & 0.5987 \\  & no\_relative\_position & 0.5981 & 0.5860 \\  & no\_feedforward & 0.5658 & 0.5672 \\  & no\_residual\_connections & 0.5762 & 0.5657 \\  & no\_layer\_normalization & 0.5741 & 0.5755 \\  & no\_dropout & 0.6059 & 0.5932 \\ \hline \end{tabular}
\end{table}
Table 6: _Cont._  
\begin{table}
\begin{tabular}{l c c c} \hline
**Component Removed** & **Avg Impact** & **Std Dev** & **Count** \\ \hline no\_feedforward & 0.0453 & 0.0565 & 3 \\ no\_residual\_connections & 0.0167 & 0.0235 & 3 \\ no\_layer\_normalization & 0.0007 & 0.0006 & 3 \\ no\_disentangled\_attention & 0.0006 & 0.0200 & 3 \\ no\_enhanced\_mask\_decoder & \(-\)0.0003 & 0.0211 & 3 \\ no\_dropout & \(-\)0.0015 & 0.0226 & 3 \\ no\_relative\_position & \(-\)0.0075 & 0.0106 & 3 \\ \hline \end{tabular}
\end{table}
Table 7: Component Importance Summary Across All Datasets (DeBERTa).