esa-sceva/satcom-chunk-collection huggingface dataset:
# Energy Storage Overflow-Aware Data Delivery Scheme for Energy Harvesting Wireless Sensor Networks
###### Abstract  
In Energy-Harvesting Wireless Sensor Networks (EH-WSNs), energy storage with limited capacity is used in the nodes to store the harvested energy. Energy storage overflow (ESO) happens when the energy storage is full, which causes the nodes to be unable to store the newly harvested energy. In traditional data delivery schemes, there is the problem of \"energy hungry and surplus coexistence\", meaning that some nodes in the network are hungry for energy while some other nodes continue to waste energy due to ESO. To alleviate this problem, in this paper, we present the ESO-aware multiple path (EAMP) data delivery scheme so that more data can be delivered to the sink. With the EAMP, multiple disjoint paths from the source node to the sink are constructed, and the source node splits data into multiple pieces with each going through one of the paths, which helps in mitigating ESO. Simulation results show that the proposed EAMP scheme can deliver more data than the existing ones.  
energy harvesting wireless sensor network; data delivering; energy conservation; energy storage overflow +
Footnote †: journal: Sensors  
1
# Time-Dependent Vehicle Routing Problem with Drones Under Vehicle Restricted Zones and No-Fly Zones
## 5 Algorithm Design and Implementation
### Genetic Operations
#### 5.3.2 Selection  
The selection operator combines dynamic elitism preservation with roulette wheel selection. A self-learning mechanism is introduced to dynamically adjust the proportion of elites retained in the population based on the fitness of the population in each generation, as shown in Equation (42). Here, \(\rho(t)\) represents the elite ratio in generation \(t\), \(\rho_{\text{min}}\) is the minimum elite ratio, \(\rho_{\text{max}}\) is the maximum elite ratio, \(f_{\text{avg}}(t)\) is the average fitness of the population in generation, and \(f_{\text{best}}\) is the fitness value of the current best individual in the population.  
\[\rho(t)=\rho_{\text{min}}+(\rho_{\text{max}}-\rho_{\text{min}})\cdot\left(1- \frac{f_{\text{avg}}(t)}{f_{\text{best}}}\right) \tag{42}\]  
Figure 5: Initial solution generation.  
For the remaining individuals, roulette wheel selection is used, as shown in Equation (43).  
\[p_{i}=\frac{f_{i}}{\sum_{i=1}^{n}f_{i}} \tag{43}\]
# City Information Modeling: State of the Art
## 5 Discussion
### Directions of Development  
There is room for the conceptual, technological, and application development of CIM. The potential developmental directions of CIM are summarized as follows:  
1. Improvement of Theories and Platform Construction The current global research on CIM is distributed and explorative without an acknowledged theoretical framework, which limits the development of CIM-based applications. Therefore, studies on theories related to CIM should be conducted. In contrast, the construction of a platform related to CIM can facilitate CIM development. For example, the urban database will facilitate data management in the CIM, and the IoT monitoring network will extend the CIM content [91; 92]. However, a CIM-oriented platform is hardly found. Thus, research on the CIM platform is also a development direction.
2. Collection, Integration, and Updating of data As the core of CIM, the issue of data has drawn significant attention from scholars worldwide [92]. However, these issues, including the collection, integration, and updating of data, are still challenging because of the various types, complex structures, and massive volumes of data in the CIM [93]. Therefore, collecting, integrating, and updating data will be key future research directions for CIM.
3. Application Techniques The future of CIM applications is promising, but related techniques, such as analysis and display, are not fully developed. The development of application techniques can make full use of the advantages of CIM to solve specific engineering and management problems of cities and expand the scope of CIM application [94; 95; 96]. For example, Helsinki opened its CIM data and encouraged researchers to develop CIM applications [64; 65], which promotes the development of the smart city in Helsinki. Hence, the corresponding application techniques require further exploration.
4. Combination with Emerging Techniques The rapid development of emerging techniques, such as big data [97; 98; 99], edge computing [100; 101], and machine learning [102; 103], can facilitate breakthroughs in CIM. With these emerging techniques, CIM can further enhance the construction of smart and digital twin cities.
# Cancer Treatment: An Overview of Pulsed Electric Field Utilization and Generation
## 6 Reversible and Irreversible Electroporation Parameters  
The pulse peak and duration are dependent on each other, i.e., a lower peak of pulses in the micro/millisecond range is required for long duration pulses, while far higher peak pulses in the kV and MV range are required for short and ultrashort nano/picosecond durations. The reason for the increase of peak voltage with reduced pulse duration is the amount of energy needed to polarize and electroporate the tissues [7]. Long pulses with a duration longer than the time constant of the membrane charging time have a direct effect on the cell membrane, while short pulses affect the intracellular membrane of the organelles. A graph adapted from [51] depicting the relation between the peak and duration is shown in Figure 5.  
To electroporate the cell and temporarily increase cell permeability, the value of the pulse peak should exceed the lower value required to achieve RE (\(V_{RE}^{th}\)) at a specific pulse duration. If the voltage value decays beyond the RE lower limit during the pulse, the permeability of pores will be lower and they will reseal faster. The same applies for IRE; thus, in order to produce permanent pores, the lower limit (\(V_{IRE}^{th}\)) should be exceeded for the minimum pulse duration. Pulses with parameters in the area between the thresholds of RE and IRE lead to large pores, which can be useful for the insertion/extraction of molecules into/from the cell [52].  
The rise and fall time have a direct effect on the electroporation process, especially for short pulses; the induced transmembrane voltage is higher for short rise times, which is more suitable for IRE [53], while for long pulses the electroporation effect is not significant.  
Many parameters influence the IRE process. As highlighted in [7] (and see Figure 5), the electric field can vary from tens to thousands of V/cm. Furthermore, the pulse duration can be as low as 100 ns and up to 300 ms, and the number of repetitive pulses can reach more than 3000 pulses per process, with different frequencies, many of which are overlooked by the literature. Consequently, the results vary considerably due to differences in parameter selection.  
Figure 5: Dependence between pulse peak and duration for RE and IRE [31; 51].
## 4 Simulation Results and Discussion
### Superior Optimization Method
#### 4.2.3 Impact of Different Loading Conditions  
In this section, the energy profit has been evaluated by considering different load levels; i.e., low load, normal load, and high load, equivalent to 80%, 100%, and 120% of peak load, respectively. The results of the profit evaluation of EH considering different load levels are presented in Figure 12. As can be seen, with a 20% increase in the demand compared to the base state, the profit has decreased from $472.13 to $236.89, and with a 20% decrease in load demand, it has increased to $763.46. Therefore, the increase (decrease) in the load demand causes a decrease (increase) in the profit of the EH.  
Figure 9: Daily changes in the profit hub.  
Figure 11: Maximum daily variations in voltage, pressure, and temperature curves.  
Figure 12: Energy profit considering different load levels.  
Figure 10: Daily power loss curve in EGHNs.
# Simplified Derivative-Based Carrierless PPM Using VCO and Monostable Multivibrator
## 6 Discussion and Analysis of Results
### System-Level Performance Impact  
Combined non-ideal effects result in substantial system performance degradation. The overall system SNR measures \(-2.88\) dB, with Pearson correlation coefficient of \(-0.1276\) for the non-ideal system compared to \(-0.4028\) for the ideal case. This represents a correlation loss of 0.2752, indicating significant signal fidelity reduction.
# Variable Speed Limit and Ramp Metering for Mixed Traffic Flows: A Review and Open Questions
## 3 Research and Implementation
### VSL in Mixed Traffic Flows
#### 3.4.2 Rule-Based VSL  
In [89], a comprehensive evaluation of the potential impacts of AVs on an existing motorway system was analyzed in various motorway operational scenarios, includingheavily congested traffic (\(>\)95% of motorway capacity), lightly congested traffic (\(\approx\)70% of motorway capacity), free-flow traffic (\(\approx\)50% of motorway capacity), and future traffic (three times more than heavily congested traffic volume) conditions. A number of MoEs were measured to reflect the changes in mobility, safety, fuel consumption and emissions caused by the deployment of AVs [89]. The rule-based VSL approach was modified to mixed traffic flow conditions and also tested in different AV penetration rates. These conditions were classified as either free-flow, light congestion, or heavy congestion. The VSL control step time was set to 5 min. The minimum time-gap was set to 0.5 s for AVs and 1.1 s for HDVs. The driver imperfection was set to 0% and 50% for AVs and HDVs, respectively. The acceptance factor for speed limits was set at 1% for AVs and 15% for HDVs. For all traffic congestion scenarios, AVs showed improvements for all MoEs, as did VSL in mixed traffic flow scenarios. For heavy traffic congestion scenario, VSL improved \(MTT\) by 14% to 26% at AV penetration rates ranging from 0% to 70%. AVs improved \(MTT\) by 43% at AV penetration rates from 0% to 100% in the no control scenario. Fuel consumption was improved by 5% to 44% in the no control scenario at AV penetration rates from 0% to 100%. The case with VSL improved fuel consumption by 10% to 31% at AV penetration rates from 0% to 70%.  
The combination of Adaptive Cruise Control (ACC) equipped SAE level 2 of driving automation AVs and VSL on the safety on s congested motorway was analyzed in [91]. The car-following Intelligent Driver Model (IDM) was calibrated for the simulation framework. The tested segment was 10 km long and contained 10 evenly spaced loop detectors. The mainline traffic demand was set to 1600 veh/h/lane, and a bottleneck was set to occur after 10 min between two consecutive detectors. \(TET\) and \(TIT\) parameters were used to assess the safety performance of the proposed method. \(TIT\) was decreased by 57.6% to 79.9% from 0% to 100% AVs in the CACC and VSL model compared with ACC-only and VSL-only model which decreased it by 34% to 76.6% and 28.8%, respectively. \(TET\) was decreased by 48.1% to 77.4% from 0% to 100% AVs in the CACC and VSL model compared to ACC-only and VSL-only models that decreased it by 39.7% to 67.8% and by 27.1%, respectively.
## 6 Carboxymethyl Cellulose (CMC)
### CMC-Based Nanofibers  
2D CMC nanofibrous wound dressings have been extensively studied due to their microstructure similarity to intact skin tissue and extracellular matrix (ECM) and their high potential as drug carriers, liquid absorbents, and moisturizers. Various strategies can fabricate CMC nanofiber dressings, including ultrasonication, casting, freeze-drying, and electrospinning [91; 104; 105]. Electrospinning is the most widely used CMC nanofiber dressing fabrication technique due to its low costs, green fabrication, simplicity, reliability, and versatility [87; 92; 106; 107]. The majority of electro-spun CMC nanofiber wound dressings either involve CMC nanofibers' fabrication with/without a type of modification or co-electrospinning of a mixture of CMC and complement materials [91; 92; 105]. For instance, the fabrication of CMC electro-spun nanofibers was the focus of one interesting study. Silver nanoparticles were added to the structure to induce antibacterial activity for wound-dressing applications [91]. Anti-bacterial studies showed that the antibacterial efficiency against several bacteria, including _E. coli_ and _Staphylococcus aureus_, was up to 100%, indicating a significant potential for wound-dressing anti-microbial capability [91]. The other approach for producing CMC-containing electro-spun nanofibers through mixing with another material with complementary properties is more prevalent in the literature, especially pronounced in recent years [87; 92; 105; 108]. Fabrication and characterization of CMC/poly (ethylene oxide) (PEO) nanofibers were investigated in a study in which the mixture of the two materials was co-electro-spun to obtain a structure with tetracycline hydrochloride as a model drug, which can be used for wound-dressing applications [105]. The drug-delivery profile identified an optimized combination of CMC 3%
# Improvement of the Transglycosylation Efficiency of a
## 3 Results and Discussion
### Screening of LnbB Variants
#### 3.1.3 Residues Conserved in LNBases Only: N259T  
Selecting conserved LNBase residues for mutation from alignment across the entire GH20 is complicated by the fact that LNBases are a subclass with a highly defined subsite \(-2\) not found in the \(\beta\)-\(N\)-acetylhexosaminidases [7], which make up the majority of the glycoside hydrolases belonging to GH20 [4]. Identifying amino acids conserved specifically among the LNBases is furthermore hampered by the fact that only two LNBases have been characterized. To aid the identification of conserved residues in LNBases, the CUPP online platform (cupp.info, accessed on 25 June 2021) [30] was used to identify proteins predicted to be similar in function to the known LNBases by peptide-based functional annotation. Thus, a multiple sequence alignment was prepared of all sequences in CUPP group GH20:7.1. This group comprises the two known LNBases and several other protein sequences all originating from the Actinobacteria class. This alignment identified \(>\)60 fully conserved residues. Zooming in on those close to the active site, the following interesting residues were identified around subsite \(-2\): E257, N259, H263 (fully conserved), E215, P466, and L574 (conserved across all sequences except one). Of these, N259 and E216 were closest to the LNB-thiazoline ligand of PDB 4 JAW [7] (\(\leq\)3.0 A) (Figure 3). N259 was selected for conservative mutation to Thr. LnbB N259T showed a maximum LNT yield of 754 uM, which is a significant improvement compared to the WT, but the level of secondary hydrolysis was high (66% of the LNT hydrolyzed after 24 h; Table 1). Recently, other mutations of N259 (N259A and N259Q) dramatically improved the transglycosylation ability of LnbB in the reaction with pNP-LNB and lactose [9], again emphasizing the importance of amino acid selection in rational enzyme design. In the WT, N259 hydrogen bonds with O2 and O3 of Gal in subsite \(-2\) (Figure 3). The mutation to Thr somewhat retains this capacity, but the distance to the ligand increases from approximately 3 to approximately 4 A, thereby likely decreasing the transition state stabilization. The mutation to Ala completely removes the interaction, whereas mutation to the much larger Gln may either sterically disturb or improve the enzyme-ligand interaction in this position. In the recently published study on LnbB engineering [9], several subsite \(-2\) mutants (H263A, H263R, N259Q, and Q190L) exhibited markedly improved transglycosylation yields (15-21%) as a result of adequately reduced hydrolytic activity, yet the best variant (32% transglycosylation yield) had the subsite \(-1\) targeting mutation W394F.
###### Abstract
Additionally, relevant information about thermophysical parameters and growth features of tumor lesions can be inferred from surface temperature distributions using inverse techniques. To the best of the authors' knowledge, other reviews on this topic are devoted to specific aspects of IR thermography applied for the diagnosis of a particular kind of cancer. Examples are the reviews dedicated to breast cancer detection authored by [PERSON] et al. [13] and by [PERSON] et al. [14]. The former addresses both the general aspects of thermography and numerical simulations, highlighting the need for realistic models, while the latter discusses mainly the geometrical aspects of thermal modeling and computer-aided diagnostic tools, underlining the applications of artificial intelligence algorithms. Additionally, the review offered by [PERSON] [6] discusses the camera performance and the environmental requirements, enabling the use of IR thermography as a tool for breast tumor screening. As far as skin cancer detection is concerned, the work by [PERSON] et al. [4] briefly reviews the experimental studies aimed at detecting melanoma, while the review by [PERSON] et al. [15] provides an overview of measurement set-ups and technical procedures used for skin cancer diagnosis. Contrarily to these review papers, the present review has the goal of comprehensively describing the state of the art of technical procedures, bio-heat transfer modeling, and thermogram post-processing methods, aiding IR thermal imaging for the diagnosis of both skin cancers and breast tumors. This review paper is organized as follows: Section 2 describes the thermal signatures of different malignant diseases observable through IR thermography. The updated thermographic techniques used for the diagnosis of skin cancers and breast tumors are reviewed in Sections 3 and 4, respectively. Steady-state techniques for skin cancer diagnosis are handled in Section 3.1, while the related dynamic techniques are treated in Section 3.2, in which classical dynamic IR thermography (along with the cold stimuli applied on the skin surface), lock-in thermography, and frequency-modulated thermal wave imaging method are highlighted. Regarding the thermographic techniques for breast cancer diagnosis, apart from the stationary ones (Section 4.1), particular emphasis is placed on the dynamic techniques (Section 4.2), with a special focus on rotational breast thermography. Forward multi-layer models for the bio-heat transfer in skin tissues are described in Section 5.1, while inverse modeling techniques, such as least squares minimization-based methods and alternative algorithms, are handled in Section 5.2. Section 6 is devoted to forward (Section 6.1) and inverse (Section 6.2) modeling of breast tissues. Real breast geometry-based models are particularly mentioned. Finally, thermogram post-processing methods, related to the application of both classical processing procedures and artificial intelligence algorithms for the diagnosis of skin and breast cancers, are discussed in Section 7.
# Efficient and Robust Arabic Automotive Speech Command Recognition System
## 3 Methodology
### Speech Feature Extraction
#### 3.2.1 Mel-Frequency Cepstral Coefficient (MFCC)  
While numerous techniques are available for feature extraction, MFCC is widely adopted. In comparison to alternative features, it has been demonstrated to achieve notable results [21; 22]. Additionally, it performs well in noisy environments [23].  
Figure 2: In-car voice-command acceptance rate.  
MFCCs are the coefficients that collectively represent the short-term power spectrum of a sound. Its analysis begins by applying the Fourier Transform on the frame sequence to obtain certain parameters. The process involves converting the power spectrum to a Mel-frequency spectrum, taking the logarithm of that spectrum, and computing its Inverse Fast Fourier transform (IFFT). More detailed information can be found in [29].
## 4 Packet Scheduling Schemes  
At every scheduling interval, the packet scheduler at the Medium Access Control (MAC) layer of the satellite gNodeB selects UEs and assigns the selected UEs/IoRT devices to the available resource blocks. The scheduling metric is computed for each resource block and the UE/IoRT devices with the maximum metric is then assigned to the resource block. These metrics can be interpreted as the transmission priority of each UE on a specific resource block. One of the most popular existing packet schedulers called Modified Largest Weighted Delay First (MLWDF) together with the newly proposed scheduler are considered in this work, in order to evaluate the spectrum management schemes.
# GRACE/GFO and Swarm Observation Analysis of the 2023-2024 Extreme Drought in the Amazon River Basin
## 3 Data and Methods
### Data
#### 3.1.6 Climate Index  
ENSO is a natural climate phenomenon in the tropical Pacific Ocean. The ENSO cycle consists of two major phases: El Nino and La Nina [44]. El Nino is a phenomenon of abnormally high sea surface temperature (SST) in the eastern equatorial Pacific Ocean that causes global climate anomalies, such as increased PRE along the west coast of South America, increased drought in the Indian Ocean and Australia, and warmer winters in North America [45]. In our study, the Nino3.4 index came from the National Oceanic and Atmospheric Administration. We undertook a 3-month averaging process on the Nino3.4 index.
## 4 Case Study
### Precision Verification of the Surrogate Model  
In the interval inversion model constructed in Section 4.2, the objective function \(f_{1}\) and three constraint functions are fitted using the ARO-BP surrogate model, where \(f_{1},g_{2}\), and \(g_{3}\) represent the worst structural responses, and \(g_{1}\) is the worst reliability constraint. While the surrogate model enables hierarchical decoupling and improves the inversion efficiency, it inevitably introduces error accumulation. To ensure the reliability of the inversion process, the priority is to assess the fitting accuracy of the surrogate model.  
To evaluate the model's precision, the goodness of fit for several key performance indices is calculated. As shown in Figure 8, the goodness of fit for the maximum displacement of the main cable max(\(\Delta Z\)) is 0.98289, with a maximum error of 0.96%; the goodness of fit for the maximum displacement of the girder max(\(v\)) is 0.99377, with a maximum error of 0.44%; the goodness of fit for the worst reliability \(\beta_{1}^{L}\) of the main tower is 0.99974, with a maximum error of 0.186%; the goodness of fit for the worst lateral and vertical deflection angles \(\theta^{L}\) of the short hangers is 0.99791, with a maximum error of 2.01%. In conclusion, the surrogate models involved in the inversion model exhibit high prediction accuracy, meeting the engineering requirements.