esa-sceva/satcom-chunk-collection huggingface dataset:
# Utilization of Machine Learning Algorithms
## 4 Discussion
### Gaps and Challenges Identified in the Implementation of Machine Learning in HIV Testing  
Although our analysis suggests that ML is generally more accurate than traditional HIV testing methods, it is important to note that ML algorithms are not always infallible and may not constantly outperform traditional techniques. In addition, there exist limitations, challenges, and gaps in implementing ML interventions, especially in resource-constrained countries. The study's findings reported that biases were encountered in certain training data, which caused unrepresentative results in some studies. Furthermore, most studies examined in this review utilized retrospective designs, incorporating self-reported information vulnerable to missing data and recall bias. These issues can also result in skewed models, potentially leading to inaccurate predictions and disparities in HIV testing outcomes. Our finding is consistent with a statistical review by [PERSON] et al. [82], who acknowledged that ML techniques have several advantages over traditional statistical methods, but that the use of ML algorithms poses numerous challenges. In their study, bias in unstructured data, bias algorithms, and model overfitting were deemed limitations that could impair the effective predictive performance of ML models, resulting in erroneous outcomes [82]. Reinforcing this finding, our analysis also came across underfitted and overfitted models and several studies that suffered from small sample sizes and lacked representativeness [17; 20; 29; 48; 60].  
Diversity in geographical locations also affects the generalization of models. This study highlighted that models employed in a specific region or among a particular demographic may not be transferable to other regions or populations. Moreover, insufficient expertise and the usability issues of certain models are hampering the universal applicability of ML in HIV testing interventions, especially in underprivileged settings. This explains why ML approaches are more common in developed countries. We found that most ML initiatives were concentrated in South Africa because of the region's recent technological advancements catalyzed by the intense fight against HIV.  
To achieve the UNAIDS 95-95-95 goal of eradicating HIV/AIDS by 2030 with ML interventions, certain areas need to be improved. Hence, more ML studies should be applied in prospective studies to yield more meaningful data that can accurately predict individual behavior risk without recall bias. There is also a need for more professional training in ML for improved usability and acceptability, especially in resource-constrained settings. Furthermore, more studies involving ML in HIV testing interventions should be conducted across SSA, especially in high-HIV-incidence epidemic settings [32].
## 2 Description of the Shipboard Microgrid under Study
### Renewable Energy Sources  
#### 2.3.1 Sea Wave Energy Source  
Systems known as sea wave energy (SWE) systems are those that transform energy from the sea waves into electrical energy. In this study, the SWE system was regarded as one of the shipboard MG's RES components. There are many device types for SWE, but the wave energy conversion (WEC) device is the one that is most-frequently used to integrate wave energy into the power grid. The WEC system transforms the vertical movements caused by waves into rotational motion to produce electricity. These movements result from the WEC system rising and falling relative to its anchoring system as waves come in. The equations of motion describe the behavior of a body under the influence of external forces, restoring properties, mass, and damping [44]:  
\[\sum F=m\bar{z} \tag{5}\]  
where \(m\) is the mass of the body, \(\bar{z}\) is its acceleration, and \(\sum F\) represents the total forces acting on the system. The equation for the total forces on the body in this study is given as  
\[\sum F=F_{m}+F_{hs}+W+F_{R}+F_{PTO}+F_{W_{k}}+F_{VD} \tag{6}\]  
where \(W\) is the body's weight, \(F_{m}\) is the mooring force, \(F_{hs}\) represents the hydrostatic force, \(F_{PTO}\) is the force resulting from the power take-off devices, \(F_{R}\) denotes the hydrodynamic forces, \(F_{Wk}\) represents the vertical components of the forces generated by the incoming waves that excite the system, and \(F_{VD}\) represents the vertical viscous drag force.  
Without taking into account the SWE's non-linearity, Relationship (8) is used to represent the SWE's linearized transfer function [39; 44]:  
\[G_{SWE}(s)=\left(\frac{1}{1+T_{g2}s}\right)\left(\frac{1}{1+T_{h}s}\right) \tag{7}\]
# Real UAV-Bird Image Classification Using CNN with a Synthetic Dataset
## 3 Materials and Methods
### Corner Detection and Nearest Three-Point Selection Layer (CDNTS)
(**a–d**) synthetic RW-UAV and bird images samples for the training process; (**a1–d1**) transformed shape by CDNTS layer of a–d labeled images; (**e,f**) synthetic RW-UAV and bird images samples for the test process; (**e1,f1**) transformed shape by CDNTS layer of e,f labeled images; (**g–j**) real RW-UAV and bird images samples for the test process; (**g–j1**) transformed shape by CDNTS layer of g–j labeled images. A schematic representation of the CDNTS layer nearest three-point selection process is shown in Figure 7. The letters \(y\), \(x\), \(c\), and \(L\) defined on the graph refer to the image pixel vertical coordinate, image pixel horizontal coordinate, detected corner, and distance between vertices, respectively. The formula for calculating the variable \(L\) is given by Equation (5). \[L_{c_{n}->c_{t}}=\sqrt{(x_{cn}-x_{ct})^{2}+(y_{cn}-y_{ct})^{2}}\;, \tag{5}\]  
where \(L_{cn>ct}\), \(x_{cn}\), \(x_{ct}\), \(y_{cn}\), and \(y_{ct}\) values are the vector length between selected corner and target corner, selected corner x axis position, target corner x axis position, selected corner y axis position, and target corner y axis position, respectively. The distance calculation formula was repeated in a loop to determine the distances of all vertices to other corners. In the loop, the closest point was searched for, and a line was drawn to the nearest corner. A previously drawn line could be re-drawn in case two vertices were the nearest corners to each other. This overlap process is shown in the schematic representation with a double-sided arrow. The one-sided arrows show the starting and ending points of the lines between the corners.
## 2 Results and Discussion
**A**: more detailed reports were obtained from  
The peak areas corresponding to the studied volatiles were measured, and their average in the twelve studied _proas_ is presented in Table 1. The ratio of the peak area to odor threshold values in water (OT, in the Log\({}_{10}\) form) was used as a screening method to identify the most relevant volatiles to the aroma of _proas_. Calculation of the odor activity value (OAV, ratio of the concentration to OT) is often carried out to evaluate the most aroma-active compounds in a food product. A compound might be sensed when OAV > 1 (or >0, when the Log\({}_{10}\) form is considered) [18; 30; 32; 33]. Although differences in peak areas among different compounds do not give direct information about their relative concentration, they greatly varied (sometimes more than 10.000-fold), suggesting they were present in very distinct concentrations. Thus, the higher the ratio, the higher the probability of a compound contributing to the aroma of this bread. The table also includes some examples of foods where the compounds have been detected (visually represented in Figure S1 from Supplementary Materials). The results will be presented as follows: firstly, the advantages of the analysis of _proas_ volatile compounds by GC\(\times\)GC-ToFMS will be discussed by providing examples that allow the identification of unreported compounds. Secondly, the compounds will be explored according to their chemical families, and those which may be more relevant to the aroma of _proas_ will be suggested. Finally, the results from the analyzed samples will be compared, and the correlations among their volatiles will be studied to elucidate which compounds may be responsible for the differences in their aroma.
# Distributed Ledger Technologies and Their Applications: A Review
## 2 Distributed Ledger Technology
### Tangle  
Despite all the benefits of Blockchain technology, this technology can only be used for high-power systems. Specifically, using Blockchain in Internet of Things (IoT) networks poses a number of problems. To solve these problems, Tangle was proposed specifically for IoT devices [82]. Tangle technology is a decentralized encrypted network for recording data in a scalable and secure manner. To track history and validate the network, Tangle uses a non-cyclical directional graph to link transactions together [83]. Tangle's first technology is IOTA, which is built for IoT networks. Key features of IOTA are scalability, zero-fee transactions and its lightweight proof of work based on the Hashcash algorithm, which fits IoT devices with limited resources.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Name** & **Permission** & \begin{tabular}{c} **Consensus** \\ **Algorithm** \\ \end{tabular} & **Scalability** & \begin{tabular}{c} **Transactions** \\ **per Second** \\ \end{tabular} & \begin{tabular}{c} **Smart** \\ **Contract** \\ \end{tabular} & **Applications** & **References** \\ \hline Bitcoin & Public & PoW & Low & 5 & No & Financial & [1] \\ \hline Ethereum & Public & PoW/PoS & Low & 15 & Yes & \begin{tabular}{c} Industries, \\ Financial, \\ Insurance, \\ Voting, etc. \\ \end{tabular} & [74] \\ \hline Litecoin & Public & \
\begin{tabular}{c} Scrypt hash \\ algorithm \\ \end{tabular} & Medium & 55 & Yes, OmniLite & Financial & [75] \\ \hline Bitcoin Cash & Public & PoW & Medium & 300 &
\begin{tabular}{c} Through side \\ chains such as \\ smartBCS [76] \\ \end{tabular} & Financial & [77] \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison between different Blockchains.  
Each user who wants to perform transactions also needs to validate other transactions, so the key difference between ITA and Blockchain technology is there is no incentive system for miners to validate other transactions; therefore, to perform a transaction a user only needs to validate two other transactions. Further, if these two transactions are deemed invalid, the new transaction will also be considered invalid. This results in requiring no fees for performing thousands of transactions between IoT devices. Having more users on the network will also result in higher scalability on the network since the validating job is distributed among all users in the network [82; 83; 84]. Figure 3 illustrates the validation of transactions in IOTA.
# A Review of Practice and Implementation of the Internet of Things (IoT) for Smallholder Agriculture
## 3 Results and Discussion
### Discussion of Challenges and Recommendations
#### 3.3.4 Feedback and Implementation  
Smartphone penetration: Many agriculture advisory mobile apps are designed for use on a smartphones; however, smartphone penetration is low among rural populations in GFSS countries [89; 90].  
_Project managers_: Make sure the output is reaching the intended audience in a format that is accessible to them. If smartphone penetration is low, then using another medium such as radio, television, local print media, or extension agents can be appropriate to provide recommendations to farming communities [17]. Early discussions with farmers, farmer co-operatives, and agriculture extension agents could help to make sure that the output is reaching the audience through the right communication channel.  
Remote location: Many smallholder farms are in remote, difficult-to-access locations, which can add significant cost to IoT services if technicians need to regularly visit farms.  
_Technologists_: Incorporate onboard diagnostics for your device (e.g., battery voltage, microcontroller temperature, accelerometer) in order to minimize maintenance visits.  
_Project managers_: Identify a local farmer or extension agent who can assist with basic sensor maintenance and troubleshooting, for example, the horticulture manager at Kikaboni Farm described in Section 3.2.  
IoT revenue generation: Identifying the best customer for the information collected is important. In many cases, there is an opportunity to provide a service along with the hardware, which can provide recurring revenue.  
_Project managers_: Many IoT for agriculture projects are initially grant funded, and determining approaches to monetize data and analytics services will help ensure project sustainability. Consider this aspect when agreeing on the terms of the funding.
## 2 Collection and Statistics of Case Histories
### Statistics of Case Histories  
#### 2.2.1 Typical Strata Where the Case History Occurred  
According to the statistics [7], the typical strata where the case histories occurred include three types: soft soil, silt and silty sand, sandy pebble and weathered rock. Cases 1-29 mainly occurred in the soft soil strata represented by silty clay, accounting for about 69.0%. Cases 30-38 mainly occurred in the silt and silty sand strata, accounting for about 21.4%. Cases 39-42 mainly occurred in the sandy pebble and weathered rock strata, accounting for about 9.5%. Therefore, it is concluded that the case histories in the soft soil strata occupy a dominant position.  
The above statistical results provide data support for the setting of strata conditions in the following numerical simulations.
## 6 Conclusions  
This study demonstrates that S1 and S2 imagery is well suited for forest monitoring in East Africa. We have illustrated that different automated classification approaches can be used to address different thematic needs. While pixel-based classification approaches are an appropriate fit for a tree cover-no tree cover task, considering neighboring pixels allows one to take more information from the spatial context into account, including a better discrimination of the trees that are outside the forest in landcover maps.  
Following the methodology that was presented in this study, recent forest maps with high accuracy can be created at a national scale and be used as a baseline for forest cover change monitoring.  
The three-fold combination of machine learning, the adequate processing of S1 and S2 data for dry forest monitoring and a custom-made reference dataset is a replicable methodology that can be potentially applied to other Eastern African countries in the context of national monitoring services.  
Conceptualization, A.V., K.K. and H.E.; methodology, A.V., K.K. and V.S.; validation, A.V., K.K. and H.E.; formal analysis, A.V., K.K. and V.S.; investigation, A.V., K.K. and V.S.; data curation, A.V.; writing--original draft preparation, A.V., K.K. and H.E.; writing--review and editing. A.V., V.S., H.E. and F.A.; visualization, A.V. and K.K.; supervision, A.V., H.E. and F.A.; project administration, H.E. and F.A. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.
## 2 Related Work
### Protocol Comparisons  
In this context, protocols (Table 1) are classified based on deployment and attributes such as RRI and RTI resolution, data channels, ML integration, and channel access methods. Our proposed solution, tailored for stable distributed networks, manages multiple data channels to enhance successful communications through a hybrid approach integrating FDMA, TDMA, and channel access control methods. Integrated within this framework is our Distributed Time Slot Anti-Collision protocol (DTS-AC), which employs TDMA for RRI mitigation and FDMA for RTI collision resolution, aligning with our aim of improving communication efficiency in distributed RFID networks.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Attributes** & **PULSE** & **DEFAR** & **MCMAC** & **CORA** & **HAMAC** & **FTSMAC** & **DMLAR** & **NFRA** & **Proposed DTS-AC** \\ \hline RRI & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ \\ \hline RTI & ✓ & ✓ & - & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ \\ \hline Distributed & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ & - & ✓ \\ \hline Centralized & - & - & - & - & - & - & - & - & ✓ & - \\ \hline ML-based & - & - & - & - & - & - & ✓ & - & - \\ \hline Multi-data channel & - & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ & - & ✓ \\ \hline CSMA & ✓ & ✓ & ✓ & - & ✓ & ✓ & - & ✓ & ✓ \\ \hline FDMA & - & - & ✓ & - & - & ✓ & ✓ & - & ✓ \\ \hline TDMA & - & ✓ & ✓ & ✓ & - & ✓ & ✓ & ✓ & ✓ \\ \hline \hline \end{tabular}
\end{table}
Table 1: Protocol comparison.
## 2 Benefits Assessment of ESS Introduction in the F/R Market  
### ESS Introduction: Benefits Overview  
The economic benefits of ESSs for F/R derive primarily from the difference in generation cost (i.e., fuel cost in $/kWh) between base and peak-loader generators. To balance the mismatch in supply and demand in the real-time operation of a power system, a certain amount of a base-loader generator's capacity (typically 5%) is reserved for power system F/R. Instead, to meet load demands, expensive peak-loader generators produce the required electric power not otherwise supplied by base load generators. In this way, use of this F/R reserve causes an increase in the power system operational fuel cost.  
However, the reserved generation amount offered by base-loader generators can be replaced by introducing an ESS for F/R. In other words, the reserved amount from base-loader generators--which are cheaper than peak-loaders--can be supplied to power systems to meet load demand. From the standpoint of power system operation, the use of ESSs for F/R facilitates the replacement of expensive peak-loader generators with cheap base-loaders.  
Figure 1 depicts the basic economic benefit of an ESS for F/R in the electricity market. One of the key factors in assessing the economic benefit of ESSs for F/R in power system operation is the estimation of future prospects for the SMP in electricity markets. The SMP is the spot market price used in electric power transactions, and it is determined by considering the most expensive generation cost of the marginal generating unit that meets the marginal demand of electricity markets. When it comes to a base load generator's reserved generation associated with F/R, the revenue lost by not selling the reserved generation can be compensated for by offering the opportunity cost (COFF), which is defined as the difference between the SMP and the base-loader generator's fuel cost. Because the SMP, or the generation cost of a marginal generator, is typically decided by the peak load generator's fuel cost, the COFF offered to base load generators for F/R can be redefined as the difference between the generation costs of peak and base-loaders. In this regard, the economic benefit of ESSs for F/R can be captured by the replacement benefit--that is, the benefit that derives from fuel cost savings on account of replacing expensive peak-loader generators with cheaper base loaders in F/R. The benefit can, therefore, be assessed primarily by forecasting future SMP (i.e., the generation cost of peak load) and base load fuel costs.
## 5 Experimental Results
### Case Study Results
The updated weights in Figure 16 reflect the reaction with the dynamic disturbance changes and convergence to the constant values as soon as the system is in the stationary state. The power of torque estimations in the parabolic case witnesses a higher value than that of the helix case, so it is possible to adapt to the fast transitions of external disturbances and ensure the dynamic motions of the Delta parallel the robot. Case 2: Helix trajectory with the external load  
The productiveness of RBF networks in the proposed controller is further performed by an external load attached to the traveling plate of the Delta robot in helix operation. The objective is to evaluate the adaptation and robustness of the proposed approach against model parameter variation. In this case, the traveling plate has added an external object with 0.2 kg. Despite an increasingly small number of errors, the PDAFS algorithm successfully overwhelms the external disturbance to achieve a similar performance compared to others in the no-load case in Figures 17 and 18 and Table 11. The control signal in Figure 18 tends to widen the boundary when the joint velocities increase the speed. Figure 14: Tracking performance in workspace of case 1: **(a)** Helix trajectory; **(b)** parabolic trajectory. Figure 15: Control signals generated by the controllers in the helix and parabolic of case 1: **(a)** joint 1; **(b)** joint 2; **(c)** joint 3 in the helix trajectory; **(d)** joint 1; **(e)** joint 2; **(f)** joint 3 in the parabolic trajectory. In addition, Figure 19 demonstrates a similar curve of disturbance torques from the RBF estimators. This action creates the reaction to the new disturbance of the control signal to catch up with the speed of tracking errors and guarantee a robust adaptive system. As a result, the adaptation of the proposed controller is validated based on disturbance torque estimation and compared to no-load cases. Despite the influence of the external disturbances generated by the joint frictions or uncertainty parameters, the PDAFS with RBF NNs attachment has presented more stability than the remaining synergetic control. \begin{table}
\begin{tabular}{c c c c} \hline
**Method** & **Joint 1 (Degrees)** & **Joint 2 (Degrees)** & **Joint 3 (Degrees)** \\ \hline PDFS (no load) & 1.0158 & 0.8063 & 1.0859 \\ PDAFS (no load) & 0.6655 & 0.5510 & 0.6872 \\ PDFS (load) & 1.1674 & 0.8641 & 1.1363 \\ PDAFS (load) & 0.5804 & 0.6156 & 0.6103 \\ \hline \end{tabular}
\end{table} 
Table 11: RMSE Comparison among the controllers and PDAFS in Parabolic Case 1. Figure 16: Disturbance torque estimation (**a**) and weight update of (**b**) joint 1; (**c**) joint 2; (**d**) joint 3 from RBFNNs in helix case 1; disturbance torque estimation (**e**) and weight update of (**f**) joint 1; (**g**) joint 2; (**h**) joint 3 from RBFNNs in parabolic case 1. Figure 17: Helix trajectory performance in joint space: (**a**) joint 1; (**b**) joint 2; (**c**) joint 3 and tracking error: (**d**) joint 1; (**e**) joint 2; (**f**) joint 3 in case 2.