esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions and Prospects for Further Development of MIPs as DDS  
The current review aimed to describe the specific physicochemical properties that render MIPs suitable for clinical use, from both a material science and biopharmaceutical perspective.  
In the light of the reported literature, MIPs have shown promising results, with high tolerability and therapeutical efficacy in various diseases; therefore, continuous efforts in further improving their multivalent features towards in vivo assessment and eventually routine clinical exploitation are worth considering. Rational design to achieve complex MIP-based drug delivery platforms brings significant contribution to the development of a more focused, efficient, and patient-centered therapy, getting one step closer to the concept of precision medicine.  
Whether designed as drug reservoirs or targeted DDS, MIPs offer superior pharmacokinetics for various APIs, leading to higher bioavailability and superior safety profiles. Moreover, an MIP-based approach offers the opportunity to revisit certain APIs with limited use, or those that have been decommissioned in the past, due to poor bioavailability at the site of action, suboptimal efficacy, or tolerability.  
The main concern regarding MIPs administration in living organisms is their biocompatibility. Current information on their safety and tolerability reveals several knowledge gaps and shortcomings in the conducted experimental designs, leaving some questions still to be addressed. Long-term biocompatibility and toxicology studies are still necessary to establish potential safety risks of MIPs under in vivo conditions and to make noteworthy steps towards their future medical use.  
Despite the rather limited number of in vivo studies previously conducted on MIPs, the contributions made to the field of drug delivery discussed in this review are encouraging and should inspire the continuation of these efforts. Nevertheless, further advances in MIP  
Figure 4: Factors affecting biocompatibility. This figure presents the main factors affecting the biocompatibility of nanoparticles (NPs), including their size, surface charge, hydrophilic coatings, potential biodegradability, and the formation of protein corona. PEG = poly (ethylene glycol), PLGA = poly-lactic glycolic-acid, PC = protein corona.  
design and synthesis should follow the \"fit-for-purpose\" concept, taking into consideration their potential clinical use from the very beginning of their development.  
Conceptualization, B.-C.I. and E.B.; writing--original draft preparation, A.-I.B. and B.-C.I.; writing--review and editing, E.B. and A.E.B. All authors have read and agreed to the published version of the manuscript.  
This work was supported by a grant of the Romanian Ministry of Education and Research, CCCDI-UEFISCDI, project number PN-III-P2-2.1-PED-2019-1288, within PNCDI III, as well as by an internal grant of luliu Hatieganu University of Medicine and Pharmacy no. 882/4/12.01.2022.  
Not applicable.  
All illustrations were created with BioRender.com.  
The authors declare no conflict of interest.
## 4 Classification of MAC Protocols Based on Channel  
This section presents the classification of MAC protocols in VANETs; oriented as distributed, centralized, cooperative, cluster-based, virtual token, and random selection based and further sub-divided as multi-channel and single-channel. The classified MAC protocols based on channel access mechanisms are depicted in Figure 3 and discussed.
## 3 Method
### Mesh-Tree Grid Map Topological Representation
#### 3.1.2 Topological Representation  
According to the assumptions listed above, our mesh-tree grid map topological representation converts the CAD drawing of an HDS system layout into a mesh-tree structure. Figure 2(a) is a layout CAD drawing, and Figure 2(b) is its simplified topological representation. In Figure 2(a), the main track, represented by a yellow belt, is the major transportation route to ship the cargos from the start position to storage areas. Sub-tracks are grouped by the green boxes between two black lines, and they are also the storage areas. In Figure 2(b), both main and sub-tracks are constructed of square grids with equal sizes. In Figure 2(a), the two red boxes at the bottom are hoisters, which lift cargos from the input docks to the storage area. The crossed main tracks with the hoister on the side are a path start position. We do not consider hoisters in Figure 2(b) per our assumptions. No non-grid movement occurs during shipping, and all transportation is finished via four-way trucks or conveyers perpendicularly. The four-way trucks and conveyers and their geometric differences are neglected in Figure 2(b). In this way, an HDS system layout is converted into a map representation.
## 2 Materials and Methods
### Methods  
#### 2.3.1 Heatwave and Drought Indices  
In this study, we adopted the non-parametric [PERSON] empirical frequency formula [50] to estimate marginal distributions in order to calculate standardized indices while avoiding parametric assumptions about data distribution. This formula, proposed by [PERSON] et al. [51], was developed under the assumption of a double exponential distribution and aims to optimize the fit of extreme values, making it widely used for probability estimation of extreme events. Based on this, we calculated SPI, SPEI, SRHI, and SSMI for different time scales (1, 3, 6, 9, and 12 months). These four indices have different meanings. The SPI mainly evaluates meteorological drought based on precipitation amounts. The SPEI, building upon the SPI, considers the impact of evapotranspiration caused by temperature and radiation. The SRHI primarily reflects atmospheric water demand by evaluating anomalies in relative humidity, making it sensitive to evaporative stress conditions. The SSMI quantifies drought by standardizing soil moisture anomalies, effectively capturing agricultural and eco-hydrological drought stress in the root zone [52; 53; 54; 55]. Several factors complicate the assessment of drought in the rubber plantations of Hainan Island. Firstly, precipitation on the island mainly comes from thermal thunderstorms and typhoon rains. Secondly, strong radiation leads to intense evapotranspiration. Third, the varied topography of the island results in diverse moisture conditions. Consequently, it is necessary to determine the most suitable drought index for rubber plantations on Hainan Island by considering multiple water sources. We assessed heatwave events using the STI, which estimates the severity and impacts of heatwaves based on Tmax data [13]. Due to the rapid occurrence of heatwave events, we only calculated the STI at a 1-month scale. The specific calculation formula is shown in Equation (1):  
\[G(x)=\frac{i-0.44}{n+0.12} \tag{1}\]where \(G(x)\) is the estimate of the marginal distribution function; \(i\) is the rank of value \(x\) in the sample data (\(i\) = 1, 2, 3, \(\ldots\), n), ordered from smallest to largest; and \(n\) is the total number of sample data.  
Then, the SI value is obtained by inverse normalization of \(G(x)\). The specific calculation formula is shown in Equation (2):  
\[SI=\Phi^{-1}(G(x)) \tag{2}\]  
where \(\Phi^{-1}\) is the inverse function of the standard normal distribution.  
According to the classification standards of the SPEI, SPI, SRHI, and SSMI, we categorized the numerical ranges for mild, moderate, severe, and extreme drought as (\(-1\), \(-0.5\)), (\(-1.5\), \(-1.0\)), (\(-2.0\), \(-1.5\)), and (\(-\infty\), \(-2.0\)), respectively. Similarly, according to the STI classification standard, we categorized the numerical ranges for mild, moderate, severe, and extreme heatwaves as [0.5, 1.0), [1.0, 1.5), [1.5, 2.0), and [2.0, \(+\infty\)), respectively.
## 5 Simulation Study and Results  
### Simulation Results  
The proposed EMS is simulated for the DC microgrid using MATLAB/Simulink. The simulation parameters are shown in Table 1. The control parameter for the EMS is PV, wind, fuel cell, battery, diesel generator, and load power. The load is subdivided into three categories: priority, commercial, and agricultural vehicle load.  
Figure 8: Flowchart for proposed energy management strategy (EMS) of the DC microgrid.  
The priority load consists of the basic requirement of load that has to be supplied continuously (street lighting of village community), whereas the commercial load and agricultural vehicle load vary according to the requirements of the consumer. Based on the available power generated by all sources with respect to load, the EMS is categorized into the following three cases:  
1. Generation equal to load (\(P_{G}=P_{L}\));
2. Generation greater than load (\(P_{G}>P_{L}\));
3. Generation less than load (\(P_{G}<P_{L}\)).
## 3 Construction of Recognition Model
### Inception-Attention Module  
The inception module [34], a classical feature extraction module, was first introduced by GoogLeNet. It captures feature information at various scales using differentiated convolution kernels and parallel pooling. These features are then concatenated in the channel dimension. This approach allows the model to simultaneously consider local details and global information at different scales, thereby enhancing its ability to understand images.  
To extract sEMG features, this paper introduces the inception-attention module, depicted in Figure 7. This module combines the inception architecture with the SE (squeeze-and-excitation) module, a lightweight attention mechanism. The SE module adaptively learns the relationships between channels, allowing for the dynamic adjustment of each channel's importance. To fit the input requirements of the inception-attention module, we resize each pre-processed grayscale image to 224 \(\times\) 224 pixels.  
The development of the inception-attention module involves the following process: Four branches are established using convolution kernels of different sizes (3 \(\times\) 3 and 1 \(\times\) 1), enabling the extraction of information across various spatial scales. Specifically, the first branch employs 3 \(\times\) 3 convolution kernels to extract features and capture local details. The two middle branches first use 1 \(\times\) 1 convolution kernels to increase the channel count, followed by 3 \(\times\) 3 convolution kernels for feature extraction. The fourth branch uses a 3 \(\times\) 3 max-pooling operation to highlight important information in the images, followed by feature extraction with 3 \(\times\) 3 convolution kernels, and then alters the channel count using a 1 \(\times\) 1 convolutional layer. To enhance the representation capability of the inception-attention module further, an SE block is integrated into each branch. The outputs of the four branches are then concatenated in the channel dimension, forming the final output of the inception-attention module.  
The four branches of the inception-attention module capture diverse features of sEMG signals across multiple scales, enhancing adaptation to the complexity of sEMG signal  
Figure 7: Inception–attention module.  
grayscale maps. Integrating the SE block into the inception-attention module allows for the reweighting of features across channels, enabling the module to adaptively learn channel relationships and optimize feature representation. Features extracted by each branch are then fused in the channel dimension, creating varied feature representations and enriching the information provided to subsequent tasks. With these improvements, the inception-attention layer is better suited to the characteristics of sEMG signal grayscale maps, thus enhancing the performance and effectiveness of the feature extraction layer in sEMG-related tasks.
## Materials and Methods  
### Encoder-Decoder Architecture  
Presently, the predominant technique for implementing deep neural networks in the field of MNT is the encoder-decoder architecture. A great number of variations have been proposed in the literature, offering solutions that make up the state of the art in different tasks [10]. It consists of two parts that collaborate with each other. On the one hand, there is an encoder that, from the input vector \(\mathbf{X}=x_{1},x_{2}\ldots x_{l}\), generates an intermediate vector \(\mathbf{Z}=z_{1},z_{2}\ldots z_{l}\) of the same length as \(\mathbf{X}\), where each column \(z_{i}\) describes the characteristics of the environment around the \(i\)-th value of the input. On the other hand, the decoder acts sequentially and, at each instant \(t\), it takes the \(i\)-th input of the intermediate vector, \(z_{i}\), and the output produced by itself at the previous instant, \(y_{t-1}\). It computes the output at the current instant, \(y_{t}\), thus forming the output vector \(\mathbf{Y}=y_{1},y_{2}\ldots y_{l^{\prime}}\), with length \(l^{\prime}\), that can be different from \(l\). The output ends when the decoder produces a special \"end of string\" symbol.  
An interesting complementary technique working in conjunction with the encoder-decoder architecture is the attention mechanism, which was introduced by [PERSON] et al. [11]. It is an effective method that allows the decoder to decide the most interesting parts of the input, i.e., what parts of \(\mathbf{Z}\) are used at each instant. This technique has proven to be effective on problems such as audio textual interpretation [12] and other problems [13]. Figure 1a shows a graphical overview of the encoder-decoder architecture with an attention mechanism.  
In our case, the attention model is a fully connected neural network (FCNN) with 1 hidden layer and \(l\) output values, where \(l\) is the size of the input. The input to this part is the intermediate code of the encoder, \(\mathbf{Z}\), and the vector of hidden states of the decoder in the previous step, \(h_{t-1}\).  
Additional techniques are used to improve the effectiveness of the system, such as dropout [14; 15] (randomly removing some neurons with a given probability), weight normalization [16] (regularizing the weights of the neuron layers), gradient clipping [17] (limiting the norm of the gradient to a maximum value), and random search of the hyperparameters [18] (performing different executions of the process to find the optimal configuration of the hyperparameters of the network).
# Simultaneous Analysis of Heavy Metal Concentration in Soil Samples
## 3 Results
### Qualitative Description of FT-IR Spectra
(2015) considered the following ranges of concentrations in mg/kg: Cu 5.5 to 92.24, Cd 0.01 to 0.73, Pb 0.90 to 49.60, Zn 6.60 to 213.11, and As 0 to 19. Maximum contents of this metals in this paper were: Cu 2840, Cd 7.70, Pb 2840, Zn 1530, and As 620 mg/kg. Similarly, [PERSON] et al. (2015) for lead and [PERSON] et al. (2015) for cadmium considered lower concentrations in soil. [PERSON] et al. (2007) investigated zinc concentration up to 4601 mg/kg which is much higher than in our paper. They reported a value of standard error of cross validation of 770 and a standard error of calibration of 436. Our results could be partly compared to the results presented by [PERSON] et al. (2004) for lead (max. ca. 2000 mg/kg - in our paper the equivalent is 2840 mg/kg) and nickel (max. 80 mg/kg - in our paper 67 mg/kg). They reported that root mean squared deviations in NIR/MIR range were 839/662 for lead and 6.21/1.88 for nickel. The spectroscopic methods were characterized by higher values for the calibration and prediction errors than the reference techniques, i.e., the error values usually exceeded 10%. The described method based on the FT-IR spectra is not an alternative to the ICP-OES method but rather can be perceived as a supplementary method with great potential for application in the estimation and approximate monitoring of soil contamination in large areas.
# ITFDS: Channel-Aware Integrated Time and Frequency-Based Downlink LTE Scheduling in MANET
## 3 Experimental Results
### Packet Delay Verses Different UEs  
The packet delay has been predicted using the enhanced technique through the queue in the LTE networks. The resource blocks are effectively utilized for providing the frequency-based time allocation strategy. The virtual queue has been framed to analyze the total amount of incoming packets and used to modify the packets for transmission also to satisfy the delay needs. A gradient related methodology is implemented to minimize the optimization problem for every time period. Figure 5 shows the performance of packet delay verses different UEs in the simulation. The results of the simulation indicate that, as long as \(\mathrm{w_{k}}\) and \(\mathrm{tth}\) are properly adjusted, the proposed scheduler can cope well with both RT and NRT traffic simultaneously. In addition, the proposed scheduler also ensures a certain level of fairness among all the users because it is based on the PF criterion. The results of the simulation also show that the delay is increasingly as much as users are getting increase and PF suffers a largest delay in the network compares to QuAS, PSS and ITFDS. The proposed technique shows better performance than QuAS and PSS, because of different weight is allocated for different applications. QuAS and PSS allow only fair allocation so that the same weight is distributed to all UEs. The proposed ITFDS algorithm distributes various delays to different applications but it always gives the best performance compared to the others because of combination both time domain and frequency domain to scheduling. In the simulation, proper setting of weight value \(\mathrm{w_{k}}\) can reduce the delay effectively. As the result, the delay in ITFDS does not exceeds 5 ms. For high system load, the maximum of the mean RT packet delay was always equal to 0.1 s because the RT packet would be dropped if it violated the threshold of 20 rounds (i.e., 0.1 s).
## 3 Results and Discussions
### On-Chip Reverse Synthesis of Oligonucleotides
The gel image shows that the dominant sharp bands in sample lanes 2-5 have a slightly lower mobility than the band of 20 mer from the ladder, which confirms the presence of synthesized 21-mer oligonucleotides. The band intensities of the 21-mer product in all the lanes are higher, which is directly related to the amount of 21-mer sequence in the synthesized product, including the truncated products. The PAGE image also reveals multiple bands of lower intensities compared to the target band, which directly correspond to the truncated products from the same synthesis with a negligible amount. However, the sum of these negligible amounts of truncated sequences is highly comparable to the amount of target sequence. The gel electrophoresis results of 21-mer oligos support the MALDI-TOF/TOF MS analysis and it is clear from both analyses that 21-mer oligos were synthesized with a large number of truncated products. It is also clear that the it is difficult to provide an accurate yield and quality of the synthesized target sequence, even though it was calculated from the trityl cation absorbance during each cycle. Without capping step inclusion, it will not be wise to predict the yield accurately only on the basis of trityl cation absorbance. Therefore, it is highly recommended that the synthesis conditions are optimized further for synthesizing the long chain oligos (>15 bases) with high purity. From the oligo synthesis (<15 bases) results, it is explicit that such negligibly low amounts of truncated products are expected to not interfere with the application of the synthesized oligos. We found that, at this length, the resulting oligonucleotides were of a sufficiently high quality, but we still required further analysis and optimization in the case of long chain oligo synthesis on-chip. Fortunately, however, many biochemical applications do not have stringent purity requirements, and if the coupling efficiency is high enough, a mixture of produced products can often be used with either minimal (desalting) or no purification [27].
###### Abstract  
The fast development of the Internet of Things (IoT) and widespread utilization in a large number of areas, such as vehicle IoT, industrial control, healthcare, and smart homes, has made IoT security increasingly prominent. Ransomware is a type of malware which encrypts the victim's records and demands a ransom payment for restoring access. The effective detection of ransomware attacks highly depends on how its traits are discovered and how precisely its activities are understood. In this article, we propose an Optimal Graph Convolutional Neural Network based Ransomware Detection (OGCNN-RWD) technique for cybersecurity in an IoT environment. The OGCNN-RWD technique involves learning enthusiasm for teaching learning-based optimization (LETLBO) algorithms for the feature subset selection process. For ransomware classification, the GCNN model is used in this study, and its hyperparameters can be optimally chosen by the harmony search algorithm (HSA). For exhibiting the greater performance of the OGCNN-RWD approach, a series of simulations were made on the ransomware database. The simulation result portrays the betterment of the OGCNN-RWD system over other existing techniques with an accuracy of 99.64%.  
Keywords:cybersecurity; ransomware; Internet of Things; feature selection; deep learning +
Footnote †: journal: Applied
# Enhancing IoT Network Security Using Feature Selection for Intrusion Detection Systems
## 2 Related Work
### Machine Learning Models for Anomaly Detection  
[PERSON] et al. emphasized the influence of feature selection on accuracy when employing analysis of variance and ensemble machine learning techniques [16]. Accuracies of 96.92% and 99.77% were achieved using the UNSW-NB15 and NSL-KD datasets, respectively, thus justifying the combination of feature selection criteria and ensemble learning models.  
[PERSON] et al. explored feature selection techniques for IoT attack classification. Using OneVsRest (OVR) with machine learning models, such as extreme gradient boosting and random forest, their approach achieved 98.89% accuracy and optimized prediction times, highlighting its utility for real-time intrusion detection in IoT networks [17].  
[PERSON] et al. demonstrated the importance of ensemble machine learning models for IoT anomaly detection. Leveraging Bayesian hyperparameter optimization, their method addressed the heterogeneity of IoT datasets while significantly improving predictive accuracy, showcasing the role of feature selection in robust IDS frameworks [18].
## 1 Introduction
### Rationale  
As IoT networks become more widespread, the demand for advanced, energy-efficient, and sustainable solutions intensifies. Improved energy efficiency directly impacts operational costs, environmental sustainability, and the usability of IoT systems. Specifically, lowering power consumption is crucial to reducing environmental impacts such as air pollution. Enhancing energy performance could also incentivise broader adoption of IoT by government entities, including the Ministry of Health and the Ministry of Transportation, to enhance service delivery across sectors, from public health to transportation. Sustainability considerations are essential, not only for lowering costs but also for encouraging long-term investments in IoT technology.