esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Future Research Directions  
The integration of unmanned aerial vehicles (UAVs) into diverse sectors highlights the burgeoning need for robust cybersecurity measures and advanced forensic methodologies. This research has underscored the multifaceted nature of drone technology, where security concerns are as dynamic as the applications they support. As drones become integral to operations in agriculture, urban management, and public safety, the sophistication of cyber threats targeting these systems also escalates. The discussion of future research directions is thus critical to stay ahead of potential vulnerabilities that could compromise UAV operations and the data integrity they maintain.  
Advancements in artificial intelligence (AI) and machine learning offer promising avenues for enhancing UAV security. Future research could focus on developing AI-driven anomaly detection systems that operate in real time to identify and mitigate threats before they can cause significant damage. These systems would benefit from continuous learning algorithms that adapt to new and evolving security threats, thereby maintaining the effectiveness of UAV operations across various environments.  
Tamper-proof data chains for UAVs: such research would need to address scalability and efficiency and ensure that blockchain implementations do not impede the operational performance of drones, especially in time-sensitive applications like emergency response and real-time surveillance.  
Standardization of forensic processes in the UAV domain remains a critical need. Research should aim to develop standardized protocols that not only facilitate effective forensic investigations but also ensure compliance with international laws and regulations. This includes creating guidelines for data collection, storage, and analysis that respect privacy concerns while providing clear and actionable insights during forensic investigations.  
Furthermore, as the application of drones continues to expand, the development of predictive security measures to counteract future threats becomes more imperative. Research into predictive analytics frameworks that utilize vast amounts of operational data to forecast potential security breaches could significantly advance proactive security measures in UAV operations.  
Collaboration across academic, industry, and regulatory domains is essential for advancing UAV security. Future research should encourage collaborative frameworks that allow for the sharing of knowledge, tools, and strategies. Such partnerships could accelerate the development of innovative solutions and help standardize security measures across borders and sectors.  
In summary, enhancing UAV security and forensic capabilities requires a proactive and collaborative approach to research and development. By advancing AI and blockchain applications, standardizing forensic processes, and exploring predictive security measures, the UAV industry can safeguard against current and future cyber threats, ensuring the safe and efficient operation of drones in various sectors.
# Evaluation Methodology for Circular and Resilient Information Systems
## 4 Discussion
Furthermore, and in relation to the evaluation of the CRIS from the end-user perspective, results indicate that a CRIS system should be evaluated on its learnability, usability, and user experience, as the system is novel and the success of its application and day-to-day use will rely on the ability of the involved users to easily understand it and seamlessly utilise it in their decision-making process. Furthermore, the ethical use of data and sensitive information protection, as well as transparency and regular communication for stakeholders on CRIS impact, are essential considerations for safeguarding trust among parties and avoiding any potential negative long-term impact. To achieve this, CRIS must be combined with policies and frameworks regarding data collection, storage, access, and sharing that comply with privacy regulations. For example, consent and data anonymisation techniques could mitigate risks among businesses or individuals that share data. In our study, all data collected and analysed are based on stakeholders' consent and anonymity when deemed necessary. Additionally, the outcome of a CRIS evaluation can allow businesses to understand the positive or negative impact and decide whether they take value from sharing their data with other actors in the supply chain. Finally, examining the impact of CRIS on various dimensions may be crucial for various industries. When our methodology is adjusted to a particular case, the potential impacts on economic growth, social welfare, and the environment should also be considered. To this end, the set of KPIs derived from the study of [PERSON] et al. (2020) can also be taken into account. This study categorises manufacturing KPIs based on the sustainability triangle in terms of economic, social, and environmental pillars. Overall, this research presents an evaluation methodology for novel circular and resilient information systems (CRISs) that utilise established and emergent technologies to enable both circularity and resilience in manufacturing. It sets the groundwork for a two-fold evaluation approach (pilot-specific and technology-specific) to account for all needed aspectsthat will enable a rigorous evaluation approach. Its findings included 15 pilot-specific KPIs for three different circular value networks, 11 technology-specific KPIs for the system and sub-component, and three dimensions for end-user evaluation. Its outputs can thus be utilised by relevant information systems that are developed to enable manufacturing to introduce parallel circularity and resilience in their day-to-day operations.
# A Modified Bio-Inspired Optimizer with Capsule Network for Diagnosis of Alzheimer Disease
## 4 Results  
In the context of AD detection, both the segmentation algorithm (fuzzy-based superpixel clustering) and classification model (MGTO-CapsNet) were implemented utilizing the MATLAB (2022a) tool, especially with the help of medical image analysis and medical imaging toolboxes. This framework was executed on a computer with the Windows 11 (64-bit) operating system, AMD's RX 6900 XT graphics card, a 4 TB hard-drive, and 128 GB memory. Eight evaluation measures, namely accuracy, Jaccard similarity coefficient (JSC), sensitivity, fall-out, phi coefficient, pixel accuracy (PA), specificity, and Dice similarity coefficient (DSC) were utilized to analyse the superiority of both the fuzzy-based superpixel-clustering algorithm and MGTO-CapsNet model on the ADNI, real-time, and AIBL datasets.  
The common evaluation measures utilized for investigating image segmentation algorithms are PA, JSC, and DSC. The PA is a simpler evaluation measure used to estimate the pixel-wise accuracy, whereas, both JSC and DSC are concentrated on spatial accuracy and region overlap. The formulas used to calculate PA, JSC, and DSC are specified in Equations (14)-(16).  
\[PA=\frac{\sum\limits_{i=0}^{K}{pixel_{ii}}}{\sum\limits_{i=0}^{K}{pixel_{ij}}} \tag{14}\]  
\[JSC(PS,GS)=\frac{\left|PS\cap GS\right|}{\left|PS\cup GS\right|} \tag{15}\]  
\[DSC(PS,GS)=\frac{2\times\left|PS\cap GS\right|}{\left|PS\right|+\left|GS\right|} \tag{16}\]  
where, class is represented as \(K\), total pixels in a class is indicated as \(pixel_{ij}\), misclassified pixels in a class is denoted as \(pixel_{ii}\), pixels present in the ground-truth region are indicated as \(GS\), and the pixels present in the segmented region are denoted as \(PS\). On the other hand, the evaluation measures, namely specificity, phi coefficient, accuracy, fall-out, and sensitivity were utilized to analyse different aspects of the MGTO-CapsNet model's performance. The general formulas of these evaluation measures are presented in Equations (17)-(21).  
\[Specificity=\frac{TN}{TN+FP}\times 100 \tag{17}\]  
\[Accuracy=\frac{TP+TN}{TP+TN+FP+FN}\times 100 \tag{18}\]\[Sensitivity=\frac{TP}{TP+FN}\times 100 \tag{19}\] \[Fall\ out=\frac{FP}{FP+TN}\times 100\] (20) \[MCC=\frac{TP\times TN-FP\times FN}{\sqrt{(TN+FN)(TN+FP)(TP+FN)(TP+FP)}} \times 100 \tag{21}\]  
where, \(FP\) indicates that the MGTO-CapsNet model incorrectly detects subjects without AD, \(FN\) indicates that the proposed model incorrectly detects subjects with AD, \(TP\) indicates that the MGTO-CapsNet model correctly detects subjects with AD, and \(TN\) indicates that the proposed model correctly detects subjects without AD.
# MALDI-Based Mass Spectrometry in Clinical Testing: Focus on Bacterial Identification
## 6 Direct Identification from Other Matrices (Cerebrospinal Fluid, Urine)  
### Rapid Identification from Spinal Fluid Specimens  
The spinal fluid is examined for color, clarity, and the presence of suspended particles. Under normal conditions, the spinal fluid is clear and colorless, but in meningitis, it may be cloudy white (increased white blood cells), yellow (increased protein), or red, indicating hemorrhage. In some cases, centrifugation is performed to observe the color of the supernatant. Then, we use a microscope to look at the cells (red blood cells and white blood cells) that are present. Glucose, proteins, albumin, and globulins such as IgG are measured. We also culture the bacteria and use special stains to look for infectious agents. Direct and rapid identification is desirable for bacterial meningitis, which requires rapid diagnosis and appropriate administration of antimicrobial agents. Direct identification of bacteria in spinal fluid can provide a bacteriological diagnosis and enable early and appropriate treatment. [PERSON] et al. performed direct identification of bacteria from the spinal fluid of patients with bacterial meningococcal disease using MALDI-TOF MS [69]. Those authors reported that MALDI-TOF MS is an important technique for clinical microbiology laboratories because the method enables appropriate treatment [69]. The technique involves placing 1 mL of spinal fluid in a microcentrifuge tube, centrifuging at 13,500\(\times\)\(g\) for 5 min, and discarding the supernatant and leukocyte layer. Then, ethanol-formic acid extraction is used to process/treat the sediment (pellet) [69]. [PERSON] et al. evaluated the potential of MALDI-TOF-MS for the rapid identification of bacteria from smear-positive cerebrospinal fluid (CSF) in a cohort of meningitis patients [74]. It was suggested that MALDI-TOF-MS is useful for the rapid identification of Gram-negative rods, but not Gram-positive bacteria [74].
## 3 Results and Discussion
### Bioreactor Performance
#### 3.1.2 VFA Accumulation and Biogas Production  
Since volatile fatty acids (VFA) is an important performance indicator of the anaerobic system, the variation of VFAs with respect to seasonal temperature variations was studied in the AWFR system. It was observed clearly that acetate and propionate were the main VFAs in the effluent as shown in Figure 3, indicating that organic matter was degraded to VFAs by both hydrolytic and  
Figure 2: Influent and effluent concentrations of chemical oxygen demand (COD) and COD removal efficiency.  
acidogenic bacteria. However, no butyrate and valerate were detected, which might be due to their low concentrations below the detection threshold. During spring (days 31-90), the average effluent concentrations of acetate and propionate were 44.5 \(\pm\) 8.2 mg/L (\(n\) = 60) and 3.8 \(\pm\) 1.6 mg/L (\(n\) = 60), respectively. With the average temperature increasing to 31 \(\pm\) 3.7 \({}^{\circ}\)C (\(n\) = 90) in summer (days 91-180), the average effluent concentrations of acetate and propionate drastically dropped to 30.1 \(\pm\) 5.0 mg/L (\(n\) = 90) and 2.6 \(\pm\) 0.7 mg/L (\(n\) = 90), respectively. Nevertheless, the concentrations of acetate and propionate increased from 45.6 \(\pm\) 15.6 mg/L (\(n\) = 90) and 6.3 \(\pm\) 2.6 mg/L (\(n\) = 90) to 62.1 \(\pm\) 8.5 mg/L (\(n\) = 94) and 6.3 \(\pm\) 3.2 mg/L (\(n\) = 94) in winter. Similar results were also observed with AFBR to treat domestic wastewater in winter [36]. The effluent concentrations of both acetate and propionate were much lower than those in winter even though the OLR in summer (352 \(\pm\) 61.8 mgCOD/L/day, \(n\) = 90) was higher. This coincided with higher COD removal efficiency in summer than in winter, indicating that the conversion of VFAs to biogas was higher in summer than in winter because of high activities of the microbial community, especially for acetoclastic methanogens. Based on these findings, we further confirmed that the seasonal temperature variations affected the anaerobic metabolism of decentralized wastewater.  
The daily biogas production is shown in Figure 4. The daily biogas production in the start-up period was low, but after the formation of the stable anaerobic biofilm, it gradually increased with temperature increase, even though the HRT was reduced. The maximum daily biogas production of 10.7 L/day was achieved in summer, when the HRT was one day. This result corresponded well with a high reduction of COD concentration in effluent and a high COD removal efficiency during that period. However, the biogas production decreased with temperature decrease in winter, while acetate and propionate were accumulated. The lower biogas production in winter might be explained by poor degradation of organic matter and higher solubility of biogas at a lower temperature.  
Figure 3: Volatile fatty acids (VFAs) production in the AWFR system during the whole operation.
# Design of a Planar Cable-Driven Parallel Robot for Non-Contact Tasks
## 3 Mechanical Design
### Guidance System
#### 3.2.2 Kinematic Model  
To define the cable exit points, each of the four swivel pulleys forming the guidance system is oriented so that its swivel axis, identified by the unit vector \(\mathbf{k}_{i}\) (Figures 11b and 12b) is orthogonal to the work plane, that is a vertical plane. The two pulleys, forming the proximal or distal guidance units, are coaxial, and the projection of their swivel axes onto the vertical plane identifies the cable exit points (\(B_{i}\), for \(i=1,\dots,4\) for the proximal point in Figure 12a,b). Thus, the kinematic _standard_ model can be used without causing geometrical modeling errors. Angle \(\phi\) defines the rotation of the pulley about the swivel axis. \(\mathbf{t}_{i}\) is the unit vector of the \(i\)-th cable.  
Figure 11: Force measurement unit: (1) load cell, (2) pulley support, (3) fixed pulleys, (4) machine frame, (5) auxiliary bracket. (**a**) Front view. (**b**) Side view.  
Figure 12: Standard model definition. (**a**) Geometry of the \(i\)-th constraint of the planar 4-cable \(CDPR\). (**b**) Detail of a proximal swivel pulley.  
Considering one of the four cable transmissions (Figure 12), \(Oxy\) and \(Px^{\prime}y^{\prime}\) are defined as inertial and mobile frames. The mobile frame is attached to the platform center of mass \(P\), described as \(\mathbf{p}\) with respect to the inertial frame. Cables, assumed massless and inextensible, are considered as the line segments between the distal and proximal anchor points, defined by the pulley swivel axes (\(A_{i}\) and \(B_{i}\) respectively, for \(i=1,\dots,4\)). Points \(B_{i}\) are described by vectors \(\mathbf{b}_{i}\) in the inertial frame, and points \(A_{i}\) are described by \(\mathbf{a^{\prime}}_{i}\) and \(\mathbf{a^{\prime}}\) in the mobile and inertial frame, respectively. The rotation matrix \(\mathbf{R}(\theta)\) represents the orientation of the moving platform in the vertical plane. The platform pose is defined as \([\mathbf{p}^{T},\theta]^{T}\), and \(i\)-th cable vector is:  
\[\boldsymbol{\rho}_{i}=\mathbf{a}_{i}-\mathbf{b}_{i}=\mathbf{p}+\mathbf{R}( \theta)\mathbf{a^{\prime}}_{i}-\mathbf{b}_{i},\quad\mathbf{R}(\theta)\triangleq \begin{bmatrix}cos(\theta)&-sin(\theta)\\ sin(\theta)&cos(\theta)\end{bmatrix} \tag{1}\]  
For a given \(EE\) pose, the \(i\)-th cable length \(l_{i}\) and unit vector \(\mathbf{t}_{i}\) can be computed as:  
\[l_{i}=\|\boldsymbol{\rho}_{i}\|,\quad\mathbf{t}_{i}=\frac{\boldsymbol{\rho}_{ i}}{l_{i}} \tag{2}\]
# Estimation of Terrestrial Water Storage Changes at Small Basin Scales Based on Multi-Source Data
## 3 Methods  
### Division of Small-Scale Basin  
The slope runoff simulation algorithm proposed by [PERSON] et al. [40] is widely used in extracting basin features based on DEM [41; 42]. Based on the slope runoff simulation algorithm, the river network information of the GRB is extracted from the SRTMGL1. After the SRTMGL1 data are input into the model, the vector river network data of the basin are first generated by the slope runoff simulation algorithm. The basic principle is to simulate the movement path of real surface water flow to describe the catchment capacity of each pixel in the region. Then, the final river network of the region is the basin part whose cumulative flow is greater than the threshold. Using the regional hydrological station information as a constraint, the closest catchment location to these stations is searched for as the basin outlet in the calculated river network. Through the flow direction data calculated in the slope runoff simulation algorithm, all raster image elements controlled by each outlet are retraced one by one. According to the characteristics of pixel distribution, the small-scale basin data can be obtained by connecting the center points of the outermost grid in turn. In this study, we defined a basin an area of ~600 km\({}^{2}\) as a small basin.  
It is crucial to select appropriate accuracy evaluation indicators for evaluating the accuracy of small basin divisions. Among the river network accuracy evaluation methods, the match error of river system extraction is a common quantitative index, which is to superimpose the extracted river network with the real river network, calculate the fine polygon area produced by its superimposition, and then calculate its proportion in the total basin area. The calculation equation is shown as follows:  
\[D=\frac{\sum A_{i}}{S} \tag{1}\]  
\(D\) is the match error of basin system extraction, \(A_{i}\) is the area of the finely divided polygons produced by the superposition of the two river networks, and \(S\) is the total area of the basin. The research shows that if \(D\) is small, it indicates that the river network is better consistent with the real river network. In general, when \(D\) is less than 0.02, the precision is high. When \(D\) is less than 0.03, the precision is reasonable. When \(D\) is more than 0.03, the precision is poor.
# A Comprehensive Survey of the Harmony Search Algorithm in Clustering Applications
## 4 Clustering Applications of the HSA
### Image Clustering Applications  
[PERSON] and [PERSON], in [83], proposed a novel unsupervised image clustering technique using the HSA. Subsequently, they assessed the overall performance of the proposed approach by analyzing the acquired effects, which proved that the efficacy criterion automatically manages the proper route of training that describes an image. The proposed approach, which was executed with numerous productiveness rules, allowed the pleasant validity criterion to be obtained, which enabled the evaluation of the overall performance and robustness of the proposed technique. The results acquired using the proposed algorithm demonstrated its effectiveness.  
[PERSON] and [PERSON] [91] proposed a new picture clustering technique to overcome the limitations of conventional clustering methods, including premature convergence and sensibility to initialization centroids. HSA was used in this study to combine the critical additives of population methods and single solution methods in an optimization design. The proposed method employs hybridization fuzzy clustering and the HSA to develop its exploitation rule and to further improve the created solutions. Furthermore, Fourier transforms are employed to enhance the dimensions of the information in the images. The obtained results revealed that the proposed approach produced more optimal solutions in comparison to different methods.  
In [92], [PERSON] et al. proposed a new clustering approach, based on the HSA, to obtain cluster centers and consequently to design the statistics. Three commonly used datasets from the UCI repository and real images were applied to confirm the results obtained using the proposed approach. The overall performance of the proposed HSA was similar to that of preferred K-means clustering and to that of three similar optimization methods, namely the AG, PSO, and CS algorithms. The results were assessed using four evaluation measures, namely classification error, operating functions, computational time, and statistical significance evaluation. The acquired results illustrated that HSAcan effectively address clustering issues. Image problems are some of the most studied applications by using HSA, and this means that HSA can reach better results in this domain.
# Unmanned Aerial Vehicle Landing on Rugged Terrain by On-Board LIDAR-Camera Positioning System
## 5 Experimental Results
### Real-World Outdoor Experiments
#### 5.2.3 Landing Analysis
The algorithm developed in this study improved the landing search process by assessing both the site type and the terrain, which significantly enhanced the robustness and reliability of UAV landings.
# Video Multi-Scale-Based End-to-End Rate Control in Deep Contextual Video Compression
## 2 Background and Related Work
### End-to-End Video Compression  
End-to-end video coding is increasingly being studied to absorb effectiveness via deep learning methods. The DVC [17] is a neural-network-based end-to-end video coding framework, where all modules in the traditional hybrid video framework are replaced by neural networks. A comparison of the traditional coding framework and end-to-end coding framework is shown in Figure 1. In Figure 1, the traditional hybrid coding framework adopts the predict-transform architecture. In Figure 1, the end-to-end coding framework has a one-to-one correspondence with the traditional coding framework using the deep learning method.  
In [29], the DVC model receives further refinement and exhibits better coding performance. Following a framework similar to DVC, [PERSON] et al. [30] considered rate-distortion optimization when encoding motion vector (MV). In [31], a single reference frame is extended to multiple reference frames. Recently, [PERSON] et al. [32] proposed an RNN-based MV/residue encoder and decoder. In [33], the residue is adaptively scaled using a learned parameter. To further improve the end-to-end video coding framework, DCVC is designed with a conditional coding-based framework, and motion estimation and motion compensation are used to guide the network to generate contextual features. Then, the context with higher dimensions can be leveraged to carry rich information for both the encoder and decoder, which helps reconstruct high-frequency content for high-resolution videos.  
Figure 1: Coding frameworks: (**a**) traditional hybrid coding framework; (**b**) end-to-end coding framework.