esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Findings
### Market  
Six indicators have been assessed to establish Market progress: (1) size of the start-up's addressable market, access to market and priority segments; (2) depth of knowledge on the competition, whether a market intelligence process has been carried out, how often, in what way and whether the data obtained match those of the target market; (3) degree of product-market fit, clear value proposition differentiated from competitors' propositions; (4) the existence of paying customers and the commercial terms established with them; (5) the degree of knowledge of the customer's purchasing criteria and the procurement process; and (6) the existence of a go-to-market strategy and its level of development.  
Start-ups in the face-to-face acceleration programmes made more progress in the Market category than those in the virtual programmes. In both situations, the methodologies of the business coaches were similar, and the time allocated to work on market development was also similar. Presumably, the in-person networking events provided teams with more opportunities to talk to potential customers and business partners and get first-hand information about their markets and their needs.  
The greatest progress across all indicators in this category was made in relation to go-to-market strategy (\(\Delta\) = 1.0), followed by customer understanding (\(\Delta\) = 0.9), market size (\(\Delta\) = 0.9) and sales (\(\Delta\) = 0.8).
# PS-CARA: Context-Aware Resource Allocation Scheme for Mobile Public Safety Networks
## 1 Introduction
Moreover, it also requires mPC's context information to efficiently allocate the resources, which restricts the utilization of the conventional resource allocation schemes discussed in [12], because those schemes statically allocate resources to users without considering the context information of mPC. Most of the existing works on QoS-aware resource allocation such as QoS priority-based dynamic fractional frequency reuse (QoS-DFFR) [15] and QoS-based uplink power control [16; 17] are discussed before to improve the sum-rate in heterogeneous networks with fixed small cells. Similarly, interference-aware power control [18], neighbor interference situation-aware power control [19], and load balancing with selective borrowing (LBSB) [20] also focused on optimizing the system sum-rate under the same deployment scenarios. Moreover, in [21] authors presented energy-efficient power allocation and wireless backhaul bandwidth allocation for small cell networks to enhance the throughput. The literature discussed above has deficiencies because it only focused on fixed small cells and no mobility was modeled.
## 2 Materials and Methods
### Outcomes Measurement
#### 2.4.5 Statistical Analysis  
The data were analyzed using the SPSS package version 25.0 (SPSS Inc, Chicago, IL, USA). The normal distribution of the sample was analyzed by using the Kolmogorov-Smirnov test. A one-way analysis of variance (ANOVA) with repeated measurements and Bonferroni was used as a post-hoc test to evaluate statistical significance. Within-group effect sizes were calculated using the Cohen d coefficient [37]. An effect size greater than 0.8 was considered large, around 0.5 moderate, and less than 0.2 small. For all the data of the study, \(p\)-values lower than 0.05 were considered significant.
## 4 Results
### Generated Dataset  
The data generated were used in the methodology to be checked in different scenarios where there are many AGV circuits. Therefore, an experimental dataset was generated and seven AGV circuits were created. Circuits, the trolley capacity (pieces), and the service allowable time (minutes) are shown in Table 2. The experimental set began with three AGV circuits and five parking stations. The GA was implemented to these datasets and the obtained solutions are presented in Table 3. The difference is due to changes in AGV speed, distance, and production rates. The number of AGV circuits and parking stations was obtained by varying the speed of the AGV, the distance of the parking stations, and the production rate (TCY). These trials are given in Table 3.  
In the first 13 experiments, an AGV pool was not required. All AGV circuits use their production station as the parking station. In the 14 th trial, it is reasonable to create a pool at parking station 12, and AGV circuits 5, 6, and 7 should use this pool. In the 18 th trial, two pools should be created, where the first pool should be at parking station 10 and AGV circuits 1, 2, and 5 should be assigned to this pool, and the second pool should be created at parking station 11 and AGV circuits 4, 6, and 7 should be assigned to the second pool. AGV 3 was not assigned to any pool.  
Then, the simulation model was run on the Witness system to check whether production would be lost due to AGV transfer [15]. It was shown that the production demand was met and there was no production loss due to AGV transfer. These results can be obtained from machine occupancy rate results that are 100%. Thus, the AGV system is capable of transferring all parts without production loss.  
Considering example 15, the efficiency of AGV pooling can be noticed. If no AGV pool is used, nine AGVs are required. Computation was realized with Witness Simulation [15]. In the case of a transition to the pool system, the GA found the required AGV number as eight and AGV circuit 5, 6, and 7 use parking station 12. In this case, it was computed that  
\begin{table}
\begin{tabular}{c c c c} \hline
**Circuit** & **Capacity** & **a\({}_{\mathbf{o}}\) (min)** & **b\({}_{\mathbf{o}}\) (min)** \\ \hline
1 & 30 & 20 & 22 \\
2 & 20 & 8 & 10 \\
3 & 10 & 4 & 6 \\
4 & 60 & 20 & 22 \\
5 & 80 & 30 & 32 \\
6 & 10 & 40 & 42 \\
7 & 10 & 60 & 62 \\ \hline \end{tabular}
\end{table}
Table 2: Generated AGV circuits.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline
**Trial** & **AGV** & **Parking** & **TCY** & **Required AGV** & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **Explanation** \\  & **Circuit** & **Stations** & **(Parts/h)** & **Number** & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **Explanation** \\ \hline
1 & 3 & 5 & 60 & 5 & 1 & 2 & 3 & & & & \\
2 & 3 & 5 & 30 & 3 & 1 & 2 & 3 & & & & \\
3 & 3 & 5 & 80 & 6 & 1 & 2 & 3 & & & & \\
4 & 4 & 6 & 60 & 6 & 1 & 2 & 3 & 4 & & & \\
5 & 4 & 6  
the proposed approach allows to decrease required AGV number by 1 and provides 11% of performance, which underlines that AGV pooling becomes more reasonable to be used.
## 3 Performance Analysis of UOWC Links under Air Bubble-Induced Fading  
In this section, the performance of a SISO UOWC system under the proposed underwater channel in the presence of air bubbles and different water turbidity levels is analyzed using the average BER and the outage probability. Moreover, asymptotic expressions are obtained to shed light on the impact of the channel parameters on the system performance by revealing the diversity order and the coding gain.
## 4 Simulation Results
### Case 8: Fuel cost with voltage stability improvement  
The L index describes the system stability by measuring the distance of the actual state of the system to the stability limit. We are using the objective function given in Equation (22) to reduce both fuel cost and voltage stability. Simulation results, illustrated in Table 13, show that the fuel cost and L index using the proposed approach are 797.80 ($/h) and 0.11 (p.u), respectively. Compared with MSA, MDE, MPSO, FPA, and MFO approaches in Table 13, HIC-GWA has significantly reduced the fuel cost and L index.
# Power System Reliability Evaluation Based on Chronological Booth-Balieriaux Method
## 2 Literature Review
### Load Duration Curve Method  
The load duration curve (LDC) convolution method is a deterministic reliability evaluation method that simultaneously considers a demand over periods [11; 12]. The LDC convolution method regards generator failure as the occurrence of additional demand and updates the equivalent load duration curve (ELDC), similar to the COPT method. The general formulation of ELDC is expressed as follows:  
\[\text{ELDC}_{k}(x)=(1-FOR_{k})\text{ELDC}_{k-1}(x-C_{k})+FOR_{k}\text{ELDC}_{k -1}(x), \tag{5}\]\[\text{ELDC}_{0}(x)=\text{LDC}(x), \tag{6}\]  
where \(\text{ELDC}_{k}(x)\) is the probability that the equivalent demand exceeds \(x\) capacity, considering the uncertainty of generators 1 to \(k\). In (5), the initial term (\(1-FOR_{k}\) term) is the contribution of generator \(k\) to the system, whereas the subsequent term (\(FOR_{k}\) term) refers to the impact of the outage of generator \(k\) on the system. \(\text{LDC}(x)\) is the probability that system demand surpasses \(x\) capacity and is defined as 1, while \(x\leq 0\). Using \(\text{ELDC}\), the LOLP can be expressed as follows:  
\[\text{LOLP}=\text{ELDC}_{N}(ICP), \tag{7}\]  
where \(\text{ELDC}_{N}\) is the equivalent demand probability function, including the influence of all generators in the system; a loss of load occurs when the equivalent demand exceeds the system installed capacity, \(ICP\).
## 3 Methodology and Model Parameters
### Model Parameters  
By utilizing the well logging data for the 5182 m-5187 m section of Well GS10 (as shown in Figure 10), the pore pressure and three in situ stresses (overburden pressure, minimum principal stress, and maximum principal stress) were determined. The pore pressure was calculated using the Eation method [39], and the three in situ stresses were calculated using [PERSON]'s model [40]. The parameters of the numerical simulation are presented in Table 3. Due to the discrepancy in stress conditions between rock core tests and the actual formation, rock parameters were first calculated using logging data and then corrected with test results. Therefore, the rock strength parameters in Table 3 are determined by total stresses since effective stress cannot be determined through well logging interpretation.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Maximum horizontal stress/MPa & 154.08 \\ Minimum horizontal stress/MPa & 122.49 \\ Vertical stress/MPa & 142.58 \\ Formation pressure/MPa & 65.00 \\ Elastic modulus of rock/GPa & 49.52 \\ Poisson’s ratio of rock & 0.28 \\ Cohesion of rock/MPa & 20.52 \\ Internal friction angle of rock/\({}^{\circ}\) & 39.60 \\ Elastic modulus of the fillings in cracks/GPa & 25.68 \\ Poisson’s ratio of the fillings in cracks & 0.23 \\ Cohesion of the fillings in cracks/MPa & 7 \\ Internal friction angle of the fillings in cracks/\({}^{\circ}\) & 25.90 \\ Elastic modulus of the fillings in solution cavities/GPa & 3.85 \\ Poisson’s ratio of the fillings in solution cavities & 0.13 \\ Cohesion of the fillings in solution cavities/MPa & 7 \\ Internal friction angle of the fillings in solution cavities/\({}^{\circ}\) & 3.20 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Parameters for numerical simulation.  
Figure 9: Numerical simulation model (**a**) and mesh generation (**b**).
## 3 Results and Discussion
### Origin of the Selected Articles  
Our search methodology returned 33 journal papers, 21 conference papers, and 3 preprints that met the inclusion criteria. Of these, 52.5% of the papers were from Scopus and 29.5% from IEEE, with the remaining coming from the other sources, 4.9% of them coming from ArXiv. Next, we looked at the location of the first author's affiliation to obtain a sense of the geographical distribution of research on bee acoustics analysis. Figure 2 summarizes the distribution of countries of the included articles. As can be seen, most contributions came from the USA, UK, Japan, Slovenia, Italy, and Poland. In the figure, countries listed as \"others\" group all countries in which only one paper was included; representative countries in this category include Canada, France, Australia, New Zealand, Belgium, India, Latvia, Ireland, Republic of Korea, The Bahamas, Uruguay, and Germany.
## 1 Introduction  
Words with meaning \"Future\", \"Next\" and \"Upcoming\" are very popular as they indicate the design of something that will be available and most recent or future proof. Anybody can usually extend such a future for their individual purposes. Communication technology has borrowed these terms in two very popular technologies: NGN and FGI. The NGN means Next Generation Networks and it refers to the telecommunication (telco) approach in building multimedia services using IP networks. It originates from the telco industry and standardization organizations like ETSI, ITU-T and 3 GPP. Traditional telco's are throwing out SS7 signaling (Signaling System #7) and various SDH hierarchies for transport (Synchronous Digital Hierarchy), and replace those by all-IP networks with a standardized service control platform called IP Multimedia Subsystem (IMS).  
Future Generation Internet (FGI) (also known as Next Generation Internet--NGI or Future Internet) was born as an approach to deliver new services upon \"Internet 2\" or \"Beyond IP\". These networks can be designed in an evolutionary way or in clean slate approach on \"green field\". FGI has been driven by the philosophical thought that we would design the internet differently, if we could redesign the internet from scratch today.  
In an MIT Technology review [1]: \"The Internet is Broken\" (2005) [PERSON] said:  
_\"We are at an inflection point, a revolution point\"_  
_\"The Net's basic flaws cost firms billions, impede innovation, and threaten national security. It's time for a clean-slate approach.\"_  
The aim of this article is to discuss two approaches to upcoming network and service innovations: NGN and FGI. We present both frameworks from the services point of view as they are delivered to the end-user. This article has a philosophical nature, the purpose of which is to improve discussions about future networks (FN) and to demonstrate that the \"revolutionary\" internet FGI is not that different from the \"evolutionary\" telco NGN after all.  
The rest of the article is organized as follows, see also Figure 1:  
* Section 2 compares the telco and internet approaches to telecommunications and services;
* Section 3 describes NGN characteristics and how it is evolving from the Public Service Telephone Network (PSTN);
* Section 4 analyzes the need for FGI, due to internet weaknesses;
* Section 5 presents challenges for the FGI, and compares these to NGN solutions;
* Section 6 considers remaining FGI challenges and future NGN-FGI convergence to FN;
* Section 7 provides some conclusions.  
Figure 1: Structure of this document.
## 3 Test Results
### Water Content and Vane Shear Strength  
After the completion of the vacuum preloading test, the water content and vane shear strength at different radial distances from the center of the PVD were measured, as shown in Figures 8 and 9. The water content of the slurry gradually increased with increasing distance from the PVD, while the vane shear strength decreased gradually along the same  
Figure 7: Water discharge volume versus time.  
radial direction. Among all tested slurries, the untreated slurry exhibited the highest water content. At R = 30 mm, the water content of the untreated slurry reached 51.4%, and at all other measurement points, it remained above 70%, indicating the formation of a dense clogging zone near the PVD. The slurry outside of this zone could not be effectively dewatered.  
Flocculation treatment significantly reduced the water content after vacuum preloading. Among the tested flocculants, the dual use of PAC and APAM-flocculated slurry exhibited the lowest water content, reaching only 45.2% at R = 30 mm, indicating that this treatment greatly enhanced the dewatering capacity of the slurry. Additionally, the lime-flocculated slurry showed the smallest variation in water content along the radial direction, with the water content at R = 270 mm being only 3.8% higher than that at R = 30 mm. However, the water content of the lime-flocculated slurry at R = 30 mm was notably higher than that of the other slurries. This suggests that lime treatment can effectively mitigate clogging and enhance slurry reinforcement, although it limits further dewatering.  
Furthermore, the vane shear strength of the lime-flocculated slurry remained above 45 kPa throughout the entire model box, which was significantly higher than that of the other slurries. This enhancement can be attributed to the reaction between lime and slurry, which generates a large quantity of cementitious materials, such as calcium silicate hydrate (C-S-H). These materials bind to the surface of soil particles, forming a stable soil skeleton and significantly improving the structural strength of the slurry. In contrast, the vane shear strengths of the APAM-flocculated slurry and the dual use of PAC and APAM-flocculated slurry were comparable to that of the untreated slurry, with values of 6.3 kPa and 8.6 kPa  
Figure 8: Water content of slurry after vacuum preloading test.  
Figure 9: Vane shear strength of slurry after vacuum preloading test.  
at R = 150 mm, respectively. This indicates that organic flocculants had a relatively minor effect on vane shear strength.
# A Review on AI for Smart Manufacturing: Deep Learning Challenges and Solutions
## 5 Challenge: DNN Reliability
### Concept Drift Detection  
For streaming data, i.e., samples generated continuously in time order, concept drift refers to the phenomenon that the statistical properties of the data change over time. For instance, the machinery vibration intensity may attenuate due to aging, and the DNN model trained using old data for predictive maintenance may, therefore, fail to detect the anomalies.  
Concept drift can usually be detected through model prediction errors, data distribution changes, or a hybrid of both [139]. The first class of methods is based on the assumption that the model accuracy drops significantly when concept drift happens. These methods are compatible with various ML models, including neural networks. The second class compares the statistical similarities of the old and new data. The distributions of the old and new data will be derived for comparison [139]. However, the performances of the statistical methods degrade when the data are of high dimension. [PERSON] et al. detect the concept drift using model prediction accuracy for software defect prediction in [140]. [PERSON] et al. model the statistical distributions of the old data using multiple machine learning methods for concept drift detection in predictive maintenance in [141]. They assume that the machinery errors can be inferred through concept drifts. In [142], [PERSON] et al. utilize the model accuracy to detect data drift in order to adapt the model to the evolving data because of the natural degradation of machinery for energy prediction modeling applications. Compared to the methods without concept drift detection, their system can double the fit rate of the energy estimation.
# Innovative Strategies for Combining Solar and Wind Energy with Green Hydrogen Systems
## 5 Discussion
### Renewable Energy Sources
#### 5.1.3 Hybrid Solar and Wind Energy System for Hydrogen Production  
PV systems are most effective for producing electricity in rural locations where there is no grid. Furthermore, creating H\({}_{2}\)-based wind and solar energy to power electrolysis units is extremely appealing, especially in the case of excess energy, which can be traded in, stored, or transformed into electricity through fuel cells [78]. To increase the effectiveness of the H\({}_{2}\) production system, it is necessary to integrate wind and solar power in order to create an efficient hybrid H\({}_{2}\) production system, which allows for lower H\({}_{2}\) costs and constant production, since two green energy sources are used [96]. Figure 11 depicts the PV-wind/H\({}_{2}\) system concept. The PV-wind/H\({}_{2}\) system outperforms the PV/H\({}_{2}\) and wind/H\({}_{2}\) systems due to its high potential for electricity. The PV/H\({}_{2}\) and wind/H\({}_{2}\) systems combine to generate the hybrid system [97]. Hybrid systems are more efficient than single systems like PV/H\({}_{2}\) or wind/H\({}_{2}\)[98]. The efficacy of the PV-wind/H\({}_{2}\) system is improving as the input electricity to the electrolysis unit rises, as does the temperature of the water within the electrolysis.  
To improve the efficiency of the solar-wind/H\({}_{2}\) system, numerous more experiments have been carried out utilizing various methodologies. The results shown below serve as examples [4; 99; 100; 101]:  
* Refueling automobiles at H\({}_{2}\) stations, where a kilogram of H\({}_{2}\) may be produced at a price of 13.12 USD.
* Batteries increase the system performance and enable uninterrupted constant operation of the solar-wind/H\({}_{2}\) system.
* The hybrid system has a higher efficiency factor than the single system.
* Supplying homes with power with fuel cells that run on hybrid systems that produce H\({}_{2}\).
* Supplying cars with H\({}_{2}\) for as little as 9.28 USD/kg.
* At an efficiency of about 61%, the production of 239 kg/h has been attained.  
\begin{table}
\begin{tabular}{l l l l} \hline
**Country** & **Electricity Cost (USD/Kwh)** & **H\({}_{2}\) Production Cost (USD/Kg)** & **References** \\ \hline Turkey & NA & 3.10 & [88] \\ Pakistan & NA & 4.02 & [89] \\ Algeria & NA & 1.214 & [90] \\ Iran & 0.0325–0.0755 & 1.375–1.59 & [91] \\ Iran & 0.068–0.115 & 2.1008–3.5602 & [92] \\ Morocco & NA & 2.36–2.66 & [93] \\ South Africa & NA & 6.34–8.97 & [94] \\ Afghanistan & 0.063–0.079 & 2.118–2.261 & [91] \\ Kuwait & NA & 13.28–11.52 & [95] \\ \hline \end{tabular}
\end{table}
Table 5: Wind energy system based on hydrogen production and electricity costs.  
Thus, the H\({}_{2}\)-generating system's solar and wind energy can be used for desalination, electricity, cooling, and heating in addition to producing hydrogen. A summary of the features of a few hybrid solar-wind hydrogen systems is shown in Table 6.