esa-sceva/satcom-chunk-collection huggingface dataset:
# Business Models for the Internet of Services: State of the Art and Research Agenda
## 1 Introduction  
While the technological advances in the more recent past have opened up a range of new business opportunities [1], both enterprises and software vendors have struggled with the fact that the ICT (Information and Communication Technologies) systems, mostly acquired for more extended periods of time, are exposed to disruptive innovative technologies which have to be rapidly introduced in order for companies to remain competitive in the market [2]. Furthermore, the success of companies in achieving their goals depends on how they integrate the different digital technologies into their business models [3].  
New software and services are being developed at an impressive speed and deployed on the Internet, along with virtualized services that connect real-world physical resources. Services from multiple networks converge into a complex ecosystem called the Internet of Services (IoS) [4].  
The IoS gains relevance in the scientific literature as an important cornerstone of Future Internet [5], and Industry 4.0, coupled with the Internet of Things (IoT) [1, 6]. Through the IoS, the cross-organizational services are offered and used by the value chain participants [7].  
A service ecosystems perspective on business models offers rich implications for an enterprise's strategy [8]. Recent research, though, raises problems in regard to \"the adaptation of business models to services scenarios, such as the reconfiguration of value chains, the customization and new forms that business models will adopt, and the sharingresources in the network of companies operating under flexible supply chains\" [9]. The IoS may provide solutions for such challenges; however, the corresponding literature is scarce.  
While there is a great focus on IoT, there is a lack of clarity on IoS applications and contributions to the business ecosystem. Research is needed for a business model to adopt an IoS ecosystem perspective, instead of firm-centric models as servitization and digitalization. Moreover, a systematic literature review regarding IoS and its correlation with business models is still missing.  
In this sense, the aim of this paper is to highlight the research directions in the field of IoS-based business models. The study presents the results of a systematic literature review correlating the keywords Internet of Services and business model. Twenty-three different studies are presented as well as the problem they address and proposed solution. The review allows identifying the state of the art and the possible gaps addressed by a research agenda.
# Designing Dual-Function Nanostructures for Water Purification in Sunlight
## 3 Results and Discussion
### Morphological Study  
It is clear that Zn-Al LDHs in Figure 7a demonstrate a uniform and typical hexagonal characteristic of LDH's structure. It exhibited large sheets with an average diameter around 2.5 um and thickness in the nano size [37; 38]. The regularity of the plates confirms the high crystallinity presented by XRD measurement.  
Figure 6: TGA curves of: (**a**) Zn-Al LDH, (**b**) Zn-Al-GO1, (**c**) Zn-Al-GO2, and (**d**) Zn-Al-GO3.  
Figure 7: SEM images of: (**a**) Zn-Al LDH, (**b**) Zn-Al-GO1, (**c**) Zn-Al-GO2, (**d**) Zn-Al-GO3 and (**e,f**) The prepared grapheme.  
However, the SEM results displayed significant morphological differences related to the presence of GO. Interestingly, the corresponding images of the obtained Zn-Al-GO nanocomposites (Figure 7b-d) indicated that the GO with Zn-Al LDHs made unique aggregates cross-linked characters of the three-dimensional nanoflowers with smooth nanoplates [39]. The volume and distribution of the nanoflowers collections increased with increasing the amount of GO to be more tangled in the Zn-Al-GO1, while Zn-Al-GO2 was more dispersive. Consequently, the detailed features of the Zn-Al-GO could be tuned by the concentrations of GO used in the preparation process, which may control the morphology of the LDH nanoflakes [40], noting that the prepared graphene had plate-like morphology as shown in Figure 7e,f.
# A Two-Stage Household Electricity Demand Estimation Approach Based on Edge Deep Sparse Coding
## 4 Results
### Test Cases and Results
#### 4.2.2 Effect from Shallow to Deep  
A sensitivity analysis was conducted to systematically evaluate the effect of learning depth on sparse coding. The edge deep K-SVD algorithms with different depths were: (1) Subjected to the same input size, output size, and iteration configuration parameters; (2) set to \(s_{0}=5\) to limit the non-zero elements of the coefficient vector to five, and five DUBPs were extracted from the original AMI data; and (3) the averaged values of 20 tests were obtained. The results are presented in Figure 8.  
As shown in Figure 8, the proposed deep K-SVD algorithm received an obvious improvement from layer 1 to layer 2. From layer 2 to layer 3, the improvement was nominal and deep K-SVD witnessed the best performance with 3 layers, with approximately 0.08% in MAPE, 34.22 A in MAE, and 62.96 A in RMSE. Further increases in learning depth did not improve the results and the reconstruction precision reduced as the learning went deeper because of the overfitting issue.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**CR** & **Algorithm** & **RMSE (A)** & **MAE (A)** & **MAPE (\%)** \\ \hline \multirow{2}{*}{0.1250} & 3-layer K-SVD & 57.37 & 31.77 & 0.07\% \\  & 3-layer K-SVD with appliance load & 54.74 & 30.07 & 0.06\% \\  & 3-layer K-SVD & 65.66 & 35.67 & 0.10\% \\  & 3-layer K-SVD with appliance load & 62.96 & 33.22 & 0.08\% \\  & 3-layer K-SVD & 71.74 & 39.04 & 0.14\% \\
0.0833 & 3-layer K-SVD with appliance load & 69.49 & 37.73 & 0.13\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Reconstruction performance comparison with different sparse coding algorithms.  
Figure 8: Reconstruction performance sensitivity investigation regarding the number of deep sparse coding layers: (**a**) MAPE, (**b**) MAE, (**c**) RMSE.
# Threat Analysis and Risk Assessment of a Driver Monitoring System
## 2 Related Work
### Monitoring Technologies Overview  
In recent years, the focus on improving vehicle safety has led to the development of various monitoring technologies that assess the condition of the driver and passenger compartments. These systems use multiple types of sensors, including image-based cameras,radar systems, eye-blink sensors, and chemical breath analysers, to detect potential driver impairment and alertness.  
[PERSON] et al. [7] developed an approach using deep learning algorithms to process input from infrared cameras, focusing on critical indicators such as face detection, head pose estimation, eye state analysis, and gaze direction. These indicators are vital for evaluating whether a driver can take control of an autonomous system, especially in critical situations where human intervention may be necessary. In a different study, [PERSON] et al. [8] presented a system that leverages continuous eye monitoring to detect signs of fatigue, particularly by calculating the aspect ratio of the eyes. Their approach analyzes blinking patterns and eye closure duration to identify drowsiness, triggering alerts to prevent potential accidents.  
[PERSON] et al. [9] explored radar-based systems as non-contact solutions to monitor both the driver and passengers within the vehicle. Their study used a Pulsed Coherent Radar (PCR) system operating at 60 GHz, demonstrating the ability to detect seat occupancy and estimate breathing rate by analyzing subtle body movements. This technology provides accurate detection even in the presence of car vibrations, highlighting its robustness in real-world conditions. [PERSON] et al. [10] advanced this field further by using frequency-modulated continuous wave (FMCW) radar to generate point clouds for occupant monitoring. Their approach efficiently maps in-cabin environments, distinguishing between multiple passengers and their movements through advanced clustering and state machine algorithms.  
Beyond image- and radar-based solutions, other sensor technologies contribute to road safety. [PERSON] et al. [11] investigated the use of eye-blink sensors integrated with microcontrollers to detect driver drowsiness. Unlike camera-based systems, these sensors monitor eye closure duration without recording visual data, ensuring privacy. [PERSON] et al. [12] focused on breath analyzer systems integrated into vehicles to prevent drunk driving. Their system detects ethanol traces in the driver's breath, disabling the vehicle and sending alerts with location details to designated contacts.
## 2 Experimental Procedures
### Functionality of JP
#### 2.4.2 Antioxidant Properties  
The ABTS radical scavenging activity (ABTS-RSA) was assessed following [PERSON] et al. [20] protocol. Briefly, a mixture comprising ABTS (7 mM) and potassium persulfate (4.95 mM) was prepared in a 1:1 (\(v/v\)) ratio and kept in darkness at 25 \({}^{\circ}\)C for 16 h. This solution was then diluted with methanol to achieve an absorbance of 0.822 at 734 nm. Subsequently, 0.06 mL of the JP sample (diluted 1:15, \(v/v\)) was added to 2.1 mL of the prepared mixture and incubated in darkness for 10 min. The absorbance was measured at 734 nm using a UV spectrophotometer (UV-1600). Ascorbic acid (20-100 ug/mL) served as the reference standard, and the results were expressed in terms of ascorbic acid equivalent (AAE) antioxidant capacity, presented as mg AAE/100 g JP.  
DPPH Radical Scavenging Activity (DPPH-RSA)  
A slightly modified version of the protocol outlined by [PERSON] et al. [21] was employed for DPPH-radical scavenging activity (DPPH-RSA). Briefly, a 0.1 mM DPPH solution was prepared using methanol. The DPPH solution was then diluted with 100% methanol until an absorbance value of 0.876 at 520 nm was attained. Subsequently, 2.1 mL of the DPPH solution was combined with 0.06 mL of the fermented sample (diluted 1:15). After thorough vortexing, the mixture was incubated for 30 min at 25 \({}^{\circ}\)C in darkness. The absorbance was measured using a UV spectrophotometer (UV-1600) at 520 nm. Ascorbic acid (20-100 ug/mL)served as the reference standard, and the results were expressed in terms of ascorbic acid equivalent (AAE) antioxidant capacity, presented as mg AAE/100 g JP.
## 3 Methodology
### Time-Series Feature Representation
#### 3.4.1 Consistency Augmentation  
The establishment of positive sample pairs is fundamental in contrastive learning. Various augmentation strategies for general time-series tasks have been proposed in previous studies [39; 40; 41]. For supervised time-series crop classification tasks, it is essential to ensure the following characteristics: (1) preserving the magnitude of time-series values, (2) retaining the length and timestamp of the time series when exploring phenological characteristics of crop growth, (3) exploring correlations between bands given that these correlations are higher; and (4) introducing crop-type information to enhance consistency augmentation.  
Based on these assumptions, the random cropping technique in the TS2 Vec model was eliminated due to its inconsistency with the assumption (2). Then, inspired by assumptions (3), and (4) a random band masking technique and a type-wise random selection technique were implemented, respectively. Incorporating the random timestamp masking proposed by [39], we generated consistency augmentation, in which feature representations at the same timestamp in two augmented contexts with the same crop types were considered positive pairs.  
* **Type-wise random selection**  
Available type labels are high-quality-supervised information for constructing augmented contexts in contrastive learning for time-series crop classification. As shown in Figure 4, this study proposed a type-wise random selection algorithm to construct augmented contexts in batch training.  
A sample consists of a parcel-based feature vector and a crop-type label. First, in a sample batch, crop type labels (L) of each instance sample were recorded in order, and feature vectors (FV) of instance samples with the same type labels were compiled into a subset. Then, the recorded crop-type labels were replicated as augmented crop-type labels. In addition, for each crop-type label, a feature vector (FV) was randomly selected from the feature vector subset with the same crop-type labels as the augmented feature vectors. Finally, the selected feature vector was combined with the crop-type label to produce a type-wise augmented sample. Multiple instances of the same crop type labels are required for type-wise random sampling. Therefore, a batch size greater than the total number of crop-type labels was set.  
Figure 4: Type-wise random selection to construct augmented contexts in batch training. Background colors indicate different crop types.  
* **Spectral band masking**  
Spectral band masking can also be adapted to generate new contexts. For each time-series input, one spectral band was randomly selected and masked (setting their values to 0) to generate an augmented context view. Furthermore, in two context reviews, their contextual representations should be consistent. The contrastive learning framework could capture band-to-band correlations through spectral band random masking to establish inherent feature spaces for crop classification.
## 4 Prototype Implementation and Experiment
### Hopping Performance Analysis
#### 4.2.2 Hopping Performance Verification  
To verify the correctness of the theory of the mechanism and the actuator design, as well as the feasibility of using a single motor with continuous circumferential rotation in one direction to achieve continuous hop, the prototype was mounted on a vertical slide rail to constrain its lateral deflection for the avoidance of the negative effects of unsteady landing and taking-off in continuous hopping (The video file titled \"Experimental Video-RHop.mp4\", which can be found in the Supplementary Materials).  
Two prototypes were tested in the experiment, i.e., (a) the prototype equipped with the special actuator, and (b) the prototype, just equipped with a rigid motor.  
Tension springs with different stiffness were tested for whether they could achieve energy storage. When the stiffness of the tension spring was less than or equal to 398.3 N/m, both prototypes could achieve the energy storage and hopping. When the stiffness of the tension spring was greater than 455.2 N/m, neither type of prototype could achieve energy storage and hop; When the stiffness was within the range of aforementioned limit values, only the first prototype could achieve the energy storage and hopping. This proves that the robot that equipped with the special actuator could amplify its output torque with the same motor, thereby improving the hopping performance. Figure 15 shows the experimental process of the two prototypes using k = 398.3 N/m tension springs. The energy storage time required for the first prototype was 0.871 s, and it could hop up to 129 mm. However, the second prototype needed 1.236 s and could just hop up to 120 mm. Let the ratio \(\gamma\) between the gravitational potential energy at the highest point of the robot \(E_{p}\) and the elastic potential energy of the tension spring \(E_{ps}\) be defined as the energy conversion efficiency of the robotic hopping mechanism. Then, the energy equation of two prototype can be measured as \(E_{ps}\) = 1.392 J, \(E_{p1}\) = 1.062 J and \(E_{p2}\) = 0.988 J, i.e., the energy conversion efficiency of the first prototype is \(\gamma_{1}\) = 76.29%, and the second one is \(\gamma_{2}\) = 70.98%, which proves that the first prototype can greatly reduce its energy storage time and improves its energy efficiency by 5.31%. The main reasons for the loss of elastic potential energy are the friction at the hinged joint and the friction of the slide rail.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Horizontal Deviation** & **Vertical Deviation** & **Average Deviation** & **Root Mean Squared Error** \\ \hline
1.4 mm & 3 mm & 0.7345 mm & 0.8543 mm \\ \hline \end{tabular}
\end{table}
Table 3: Trajectory sampling analysis results.
# An Analysis of Cloud Security Frameworks, Problems and Proposed Solutions
## 3 Cloud Security Frameworks
### NIST SP 800-144
#### 3.2.2 NIST's Limitations in Protecting Cloud Infrastructure  
1. Rapidly emerging Threat Landscape: Threats and weaknesses to cloud security are continually emerging. Although routinely updated, the NIST framework may not always keep up with developing risks. To successfully manage the shifting threat landscape, organizations should augment the NIST framework with continuous monitoring, threat information, and industry-specific security.
2. NIST SP 800-144 has limited coverage of foreign standards and regulations since it is primarily aimed at US government organizations and may not handle international standards and regulations completely. Organizations having worldwide operations or those operating in many countries may need to consider extra regulatory frameworks and norms particular to their geographical location.
## 4 Discussion
### Study Limitations and Future Directions  
This study provides valuable insights into the acute neuromechanical responses induced by velocity-specific flywheel training. However, certain limitations should be acknowledged. The investigation was limited to short-term PAP effects within 12 min following the intervention. Whether these acute improvements translate into long-term adaptations in muscular strength, power output, and sport-specific performance remains unclear. Future longitudinal studies are warranted to explore the chronic outcomes associated with different flywheel training intensities.  
The sample population comprised only high-level male athletes within a narrow age range, which restricts the generalizability of the findings to broader demographics, including female athletes, older populations, and individuals with varying training backgrounds. Expanding participant diversity in future studies will improve external validity and enhance understanding of training responsiveness across subgroups.  
In addition, the present design did not include a comparison with other PAP strategies such as traditional resistance training, pyometrics, or isometric protocols. Incorporating comparative groups in future research would help clarify the relative efficacy and specificity of flywheel training in eliciting performance enhancements.  
Moreover, the study focused solely on performance outcomes and joint-level kinetics without integrating neuromuscular activation markers or tendon behavior measurements. The inclusion of electromyographic, ultrasound, or other physiological indices in future research could provide more comprehensive insights into the mechanisms underlying observed performance changes.
## 3 Numerical Experiments and Discussions
### Performance Index
#### 3.3.2 Position Accuracy  
Besides the availability requirements, HA/VA (95%) requirements for aviation position during typical operations are shown in Table 1. The horizon error (95%) and vertical error (95%) of our method are 7.1 m and 4.3 m, respectively, and the error (95%) of standalone GPS navigation in horizon and vertical are 8.0 m and 5.2 m, respectively. The position results show that our method can improve the accuracy and meet the HA/VA (95%) requirements of APV-I and APV-II. Recently, vision-aided positioning is being discussed for aviation applications [15], while in this paper we pay more attention to researching integrity during the approach and landing phase.
# Novel Drug Combinations in Lung Cancer: New Potential Synergies Between 5-FU and Repurposed Drugs
## 3 Results  
### The Effect of 5-FU as Single Agent on A549 Cellular Viability  
In the previous studies of our group, we developed a new combination model involving both antineoplastic and repurposed drugs [46; 47; 48].  
We start by evaluating the cytotoxic effect of the antineoplastic drug 5-FU on A549 cells, to verify its effectiveness against this type of cancer. The cells were exposed with different concentrations of 5-FU, ranging from 0.1 \(\upmu\)M to 100 \(\upmu\)M, for a duration of 48 h. The cell survival was measured using the MTT assay, which assesses mitochondrial activity, and is commonly used to determine cell viability. Cellular morphology was also observed after 48 h.  
As shown in Figure 1A, the MTT assay results demonstrated that 5-FU exhibited significant anticancer activity at concentrations above 10 \(\upmu\)M. The morphological analysis further confirmed the toxic profile of 5-FU. Consistent with the results obtained from the MTT assay, the analysis of cellular morphology revealed significant changes in the phenotype of A549 cells when exposed to concentrations above 10 \(\upmu\)M, as well as a decrease in cell density upon 5-FU treatment.  
To gain a clearer understanding of the dose-response relationship of 5-FU, a dose-response curve was generated using the data from the MTT assay (Figure 1B). From this curve, the IC\({}_{50}\) value, representing the concentration of 5-FU needed to reduce cell growth of A549 cells by 50%, was calculated. This value offers a quantitative measure of the drug's potency in inhibiting cancer cell proliferation. Interestingly, even at concentrations below 6 \(\upmu\)M, approximately 50% of the cells were already affected by 5-FU's cytotoxicity. Based on these results, a concentration of 5.03 \(\upmu\)M of 5-FU for 48 h was selected for further experiments with A549 cells (Table 1).  
The IC\({}_{50}\) value obtained in our study is consistent with the variability observed in the literature, where different studies have reported varying IC\({}_{50}\) values for the same cell line [49; 50; 51; 52]. This variability may be due to differences in experimental conditions, assay techniques, and the characteristics of the cell lines.
## 2 The Evolution from Cloud Computing to MEC
### Cloud Computing
#### 2.1.2 Service Models  
**Infrastructure as a Service (IaaS)**: IaaS is one of the first service models provided by cloud computing systems. It enables the use of available resources and servers based on user needs. With this service and the components that are provided, customers do not need to purchase and maintain the infrastructure needed to develop programs and services, storage space, etc., thus minimizing costs [23]. More specifically, the mentioned resources are like computer tools that have been turned into virtual ones. They also come with extra services. For instance, monitoring the system, keeping it safe (and not just for small businesses), distributing the workload evenly, creating backups, and more. Cherry Servers and Microsoft Azure are two examples of this type of service model.  
**Platform as a Service (PaaS)**: PaaS is useful, e.g., to website and software developers. In fact, with this service model, developers can provide, test, manage, and develop applications based on their needs and demands [24]. With this service, developers no longer need to worry about basic infrastructure such as storage space, Internet, database, and servers. This cloud computing service provides customers with a platform to perform computations, including the operating system and programming environments. Examples of this type of service model are Microsoft Azure, AWS Elastic, and Google App Engine.  
**Software as a Service (SaaS)**: SaaS is effective in providing services on the Internet platform and is provided in a shared manner based on demand and need. With this type of service, applications and software are provided to users via the Internet. The providers of these services host and manage tasks such as security enhancement, software upgrades, maintenance, and any infrastructure [25]. Examples of these popular services include tools for accounting, systems for keeping track of customer relationships, and tools for handling human resources, which are designed and developed by large companies such as Microsoft, Oracle, and IBM.