esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
The analyses presented in this paper are focused primarily on the effect of the PCM-enhanced boards studied, considering different boundary conditions, in order to definegeneral trends and the specific behavior of this technology. To this end, the simulations considered different building typologies, climates, thicknesses, and active/passive strategies. Considering all the differences that can arise with this broad number of variables, the methodology adopted to create the energy building model tries to reduce all the fluctuations related to different national laws or the specific HVAC system in order to have comparable results.  
Overall, this study highlights both general and specific trends for this technology, with interesting results. Undoubtedly, further studies could expand this research to other calibrated reference models or different climates in order to further generalize or specify the trends described in this paper. In this first work, our attention was focused on the European geographic area in order to analyze cities with similar energy containment directives. Future developments could expand this area--changing the envelope characteristics and also considering tropical and dry climates.  
The economic aspects of PCM implementation are out of the boundaries of the current study as there are very different compositions of energy and energy prices in the considered countries. Future works could focus on smaller groups of locations and estimate a payback period on selected groups of cities with similar economic and energetic backgrounds.  
Another interesting development of this first study could consider a simulation in future climatic scenarios. As shown in our research, office models are characterized by a strong cooling reduction in the active strategy and mainly by cold thermal discomfort in passive strategies. Considering future scenario simulations--characterized by warmer boundary conditions--the energy benefits should be higher than those obtained in our active strategy simulations while, on the contrary, in the passive strategy, the benefits could be lower. Finally, with regard to the midrise apartment model, the PCM benefits are related mainly to heating consumption reduction (active strategy) and the reduction of warm discomfort hours (passive strategy). Therefore, the benefits of PCM implementation could probably be reduced in future scenarios.  
Conceptualization, F.F. and A.C.; methodology, F.C., F.F. and A.C.; software, F.C.; validation, F.C. and F.F.; formal analysis, F.C.; investigation, A.S. and A.A.T.; resources, A.C.; data curation, F.C.; writing--original draft preparation, F.C.; writing--review and editing, F.F., A.C., A.A.T. and A.S.; visualization, F.C. and F.F.; supervision, F.F. and A.C. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Data Availability Statement: Data is contained within the article.  
The authors declare no conflict of interest.
## 4 Experimental Analysis and Results
### Datasets  
The evaluation was conducted on three datasets: ANERcorp, ACE 2005 (Arabic Subset), and a Custom Biomedical and Legal Dataset, as shown in Table 2. These datasets were selected to ensure comprehensive coverage across different types of text and entity structures.  
* ANERcorp is a widely used benchmark dataset that focuses on flat named entities in general Arabic news articles, including persons, locations, and organizations.
* ACE 2005, in contrast, contains more complex, nested entity structures commonly found in formal newswire texts, making it a suitable testbed for evaluating the proposed model's nested entity recognition capabilities.
* The custom dataset covers biomedical and legal documents, representing domain-specific content that requires deep semantic understanding and lexicon filtering due to specialized terminology.  
By covering these three diverse datasets, the evaluation ensures that the proposed model is tested across both general-purpose and domain-specific content, capturing a wide linguistic spectrum from formal media language to specialized professional terminology. This demonstrates the domain adaptability of the proposed approach, and future work will further extend the evaluation to additional domains such as social media, historical documents, and financial texts to further validate the model's robustness across informal and emerging text genres.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **\# Sentences** & **\# Tokens** & **\# Entity Types** & **Nested Entities** \\ \hline ANERcorp & 6000 & 150,000 & 3 & No \\ \hline ACE 2005 & 12,000 & 250,000 & 7 & Yes \\ \hline Custom Biomedical & 5000 & 80,000 & 5 & Yes \\ \hline \hline \end{tabular}
\end{table}
Table 2: Overview of datasets used for evaluation, including the number of sentences, tokens, and entity types and whether the dataset contains nested entities.  
These datasets provide a diverse evaluation environment, from simple flat entities in ANERcorp to complex nested entities in ACE 2005. For example, in the sentence \"_Stilli zi,jil j,\" (\"The deer visited the royal park\"), ANERcorp captures entities like \"_Stilli zi,jil v\" (the royal park), while ACE 2005 includes nested structures distinguishing \"_zi,jil v\" (park) and \"_zi,jil v\" (royal).
# New Approach for Brain Tumor Segmentation Based on Gabor Convolution and Attention Mechanism
## 2 Materials and Methods
### The GA Module  
Gabor filters can effectively extract local features from images. They are based on Gabor functions and are a multi-scale, multi-directional filter that can capture local features such as textures, edges, and details in images. During the training process of the network, the shallow encoder in the coding part usually deals with the lower level information and mainly focuses on the low-level features of the image, such as edges, colors, textures, etc., while the deep encoder can deal with the higher-level and abstract information of the brain tumor image, which pays more attention to the overall structure of a brain tumor and its semantic features. Therefore, in order to improve the effectiveness of the extracted low-level features for segmentation, additional feature information obtained with Gabor filtering is incorporated into the original structure of the 3D U-Net model. The GA module shown in Figure 2 is utilized for the operations associated with the Gabor filtering.  
The GA module is specifically designed as follows. The input original image is subjected to the Gabor convolution and two ordinary convolutions, and then their final structures is summed up. Since the sensory field of the ordinary convolution is limited by the size of the convolution kernel, the ability of extracting the features is limited, while the Gabor  
Figure 1: The overall structure of the proposed Ga-U-Net network, where the meanings of modules and arrows in different colors are shown in the bottom of the figure.  
Figure 2: The structure of GA module, where the component for Gabor convolution is shown in green color and the other two ordinary convolutional components are shown in blue color.  
convolution kernel consists of sinusoidal functions, with the characteristics of multi-scale and multi-direction, which makes it able to capture the image features more effectively in different frequencies and directions, and the ability to capture features is good both in the low-level encoder and high-level encoder parts; especially in the low-level encoder, the Gabor convolution decomposes the image in the frequency domain, which can better capture the local structure and texture information of the image. The feature summation of these two convolutions can enable the network to learn more comprehensive information about the tumor image. The encoder is responsible for extracting high-level abstract features from the input image, while the decoder maps these features back to the original image space to generate semantic segmentation results. The combination of encoder and decoder enables the network to simultaneously consider both global and detailed information, thus achieving good performance in image segmentation tasks. Compared with feature concatenation, the feature summation network has fewer parameters and consumes less computational resources.
## 5 Regulatory Aspects of Cyclotron-Produced \({}^{68}\)Ga Radiopharmaceuticals
### Target Transfer System and Processing  
Under PIC/S GMP Guide Annex 3 (manufacture of radiopharmaceuticals) stated that the GMP requirement is not mandatory for the cyclotron. However, the cyclotron and its transfer system may consider the first steps of active substance manufacture, which require the process to be included under the GMP element [62]. The performance qualification (PQ) for the cyclotron liquid target is straightforward and easily implemented. Cyclotron target PQ shall include \({}^{68}\)Ga EOB activity, radionuclide purity, percentage of activity lost, and volume test. For solid target production, additional steps are required where the solid target needs to be dissolved in an acidic solution, such as HCl or HNO\({}_{3}\); only then can it be further purified using single or multiple resins. There are a few options on how the dissolution process can be performed: (1) process within the cyclotron vault (in-situ); (2) process in a dedicated hot cell; and (3) process within the same hot cell where the synthesis module is located (integrated with synthesis module [39; 63; 64]. Option (1) and (3) are the most suitable for medical-grade cyclotron as such a system does not require a dedicated hot cell for the dissolution process while providing automation that reduces unnecessary radiation exposure for operators. From a GMP point of view, these processes need to be validated and included in the PQ for a solid target system. Additional testing parameters for the equipment qualification shall be considered for heating verification, consistency in the volume of acid for the dissolution process, and time verification for the whole solid target transfer and dissolution process.
###### Abstract  
Neural networks have shown significant promise in the field of image processing, particularly for tasks such as denoising and restoration, due to their capacity to model complex nonlinear relationships between inputs and outputs. In this study, we explored the application of a complex-valued neural network--a multilayer neural network with multi-valued neurons (MLMVN)--for filtering two types of noise in digital images: additive Gaussian noise and multiplicative speckle noise. The proposed approach involves processing images as a set of overlapping patches in the frequency domain using MLMVN. Training was performed using a batch learning algorithm, which proved to be more efficient for big learning sets: it results in fewer learning epochs and a better generalization capability. Experimental results demonstrated that MLMVN achieves noise filtering quality comparable to well-established methods, such as the BM3D, Lee, and Frost filters. These findings suggest that MLMVN offers a viable framework for image denoising, particularly in scenarios where frequency domain processing is advantageous. Also, complex-valued logistic and hyperbolic tangent activation functions were used for multi-valued neurons for the first time and have shown their efficiency.  
complex-valued neural networks; MLMVN; image denoising; Gaussian noise; speckle noise; frequency domain  
3
## 1 Introduction
For the physical layer, an intelligently deployed network of actuators and sensors collects information and actually controls the physical world. By converting the analog information into a digital format, the information is sent to a virtual layer input which serves as the decision-making setup. This information is further used to calculate abstract computations that feed into the real world actuation system to drive and control physical world outputs or objects. \begin{table}
\begin{tabular}{c l} \hline \hline
**Resource** & **Company/Major Distributor** \\ \hline _Sensor Nodes_ & Argo Systems, WisMote, BTnode, IMote, KMote, TinyMote, EPIC Mote. EyesIFX, FlatMesh, Mica, \\  & Telos, Iris, NeoMote, Waspmote, RedBee, Ubimote, Shimmer, WizziMote, FireFly, Bitsym Bitsense \\ \hline _Gateway Node_ & AdvanticSys, Dwara, Shimmer Span, Stargate, FlatMesh,VEmesh, DigiMesh, Bitsym Bithaul \\ \hline _Microcontrollers_ & Texas Instruments, Atmel ATmega, ARM Cortex, Thumb Microcontroller Renesas, Marvell, PIC \\ \hline _Transceivers_ & Chipcon, ATmega, ZigBit, himmer,RFM \\ \hline _Operating Systems_ & Contiki, ERIKA Enterprise,Nano-RK, TinyOS, LiteOS, OpenTag, NanoQplus \\ \hline _Programming Languages_ & C, LabVIEW, nesC \\ \hline _Software_ & Arduino API, TinyDB, TOSSIM, NS-2, NS-3, OPNET, NetSim, LinuxMCE, QualNet \\ \hline _Industry Standard_ & ANT 6 LoWPAN, DASH7, ONE-NET, ZigBee, Z-Wave, Wibree, WirelessHART, 802.15.4, MiWi \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Popular WSN platforms available for industrial and academic use [1,7].
# An Algorithm to Assist the Robust Filter for Tightly Coupled RTK/INS Navigation System
## 2 Materials and Methods  
The TC RTK/INS integration is based on an EKF consisting of two steps: prediction and update. The EKF provides floating ambiguity solutions. Readers can refer to [68] for more details about EKF. The lambda algorithm [69] is then used to search for integer ambiguity solutions. As RTK is a differential positioning technique, we first introduce the Single Difference (SD) and Double Difference (DD). Next, we describe the prediction step and the update step. Then, we introduce some key issues regarding the achievement of the fixed solution, followed by a comprehensive description of the TC RTK/INS architecture. Finally, we provide details about our novel algorithm to assist RKF.
## 4 Proposed Method for Calculating Nominal Strength of an Integrated Panel with Reinforced Studs
### Comparison of Prediction Equation and Test Results for the Integrated Panel with Reinforced Studs  
Figure 15 shows the variation in the average yield strength (\(V_{y,avg}\))-to-proposed nominal strength (\(V_{n,p}\)) with the coefficient of the stud-reinforced specimens at yield displacement \(\Delta_{y}\). The coefficient C values at nominal strengths \(V_{n,p}\) consistent with the average yield strength \(V_{y,avg}\) are 0.09 and 0.24 for the IP-IS and IP-OS specimens, respectively. The reasons for the low value of C are as follows: (1) the moment of inertia is proportional to the third power of the length in the bending direction of the member; (2) the effective section of the integrated panel is conservatively assumed, as shown in Figure 14; (3) the studs connected to the integrated member did not behave in combination with the integrated member in the IP-OS specimen, as shown in Figure 7d. The cross section of the integrated member, the cross section of the reinforcing studs, and the position of the reinforcing studs, may be factors influencing the coefficient C. Although it is unclear at this time, because only two specimens were analyzed, we believe a finite element analysis of the various models will provide a clearer correlation. In addition, as the experiment was performed on only one specimen per type of the integrated panel, the proposed equation in Section 4.1 is not perfect; however, this equation is meaningful, as it reflects the failure aspects of the specimens, such as the location of the local buckling.
# The Synthesis and Biological Evaluation of 2-(1H-Indol-3-yl)quinazolin-4(3H)-One Derivatives
## 3 Materials and Methods
### Antibacterial Activity  
The synthesized compounds were tested for their in vitro growth inhibitory and bactericidal (fungicidal) activity against _Staphylococcus aureus_ (ATCC 25923), _Staphylococcus aureus_ (MRSA, ATCC 43300), _Staphylococcus epidermidis_ (ATCC 12228), _Escherichia coli_ (ATCC 8739), _Escherichia coli_ (ATCC 25922), _Mycobacterium smegmatis_ (mc(2)155/ ATCC 700084), and _Candida albicans_ (ATCC 10231). Cefazolin, cefotaxime, chloramphenicol, amikacin, rifampicin, isoniazid, fluconazole, and Amphotericin B were used as control antimicrobial (fungicidal) agents. The cells were grown overnight at 37 \({}^{\circ}\)C in a glass tube with 5 mL of LB-broth containing 1% of glucose for _C. albicans_ and 0.05% Tween 80 for _M. smegmatis_. The grown cultures were then diluted 1:100 with fresh medium and cultivated for 5 h. The _M. smegmatis_ culture was grown for 20 h with agitation on a shaker GFL1092 (GFL, Germany) (200 rpm, 37 \({}^{\circ}\)C). Then, the cultures were adjusted to OD 0.1 (A625) and diluted to 1:10 for _M. smegmatis_ and 1:100 for the other microorganisms. The resulting suspension was used to determine the MIC using the serial dilutions method in 96-well plates with modifications. The synthesized compounds were dissolved in DMSO at a concentration of 20 mg/mL and a series of two-fold dilutions was prepared in the same solvent. Then, 10 \(\upmu\)L of the solutions was added to the wells of the plate containing 190 \(\upmu\)L of the cell suspension. To the control wells, 10 \(\upmu\)L of DMSO was added. The plates were incubated at 37 \({}^{\circ}\)C under static conditions for 24 h (72 h for _M. smegmatis_) for the minimum inhibitory concentration determination. The minimum bactericidal concentration and MFC were determined as the lowest concentration of an antimicrobial agent required to achieve a 99.9% reduction in the initial inoculum. In total, 10 \(\upmu\)L from each well of a plate for the MIC determination was inoculated on Petri dish with LB-agar (containing 1% of glucose for _C. albicans_), and colonies were checked for after incubation (24 h, 37 \({}^{\circ}\)C) to determine the MBC or MFC. The antitubercular activity of the synthesized compounds was studied with the _M. tuberculosis_ H\({}_{37}\)Rv strain using the standard BASTEC MGIT 960 growth system (Becton Dickinson) [50]; 10 mg of the test substances was dissolved in 1 mL of DMSO, then mixed with Middlebrook 7H9 medium in a ratio of 1:9 and added to MGIT tubes, obtaining a series of two-fold dilutions with final concentrations from 1 to 100 \(\upmu\)g/mL. The tubes initially contained 7 mL of Middlebrook Broth 7H9, 0.8 mL of MGIT OADC supplement, and oxygen-quenched fluorochrome, which changes the fluorescence as microorganisms grow. A total of 0.5 mL of a suspension of _M. tuberculosis_ H\({}_{37}\)Rv in physiological saline containing 5 \(\times\) 10\({}^{7}\) cells/mL was added to each tube. All the tubes were incubated at 37 \({}^{\circ}\)C for 41 days and examined for fluorescence daily.
# A Novel Fractional Sine Chaotic Map and Its Application to Image Encryption and Watermarking
## 2 Prerequisites  
This section presents definitions and theorems used in this article. Let \(\mathbb{N}_{\rho}=\{\rho,\rho+1,\)\(\rho+2,\cdots\}\) such that \(\rho\in\mathbb{R}\).  
**Definition 1** ([39]).: _Consider a real-valued function \(\Phi:\mathbb{N}_{\rho}\to\mathbb{R}\). The fractional sum of order \(\beta\) is_  
\[\Delta_{\rho}^{-\beta}\Phi(\omega)=\frac{1}{\Gamma(\beta)}\sum_{k=\rho}^{ \omega-\beta}(\omega-\kappa-1)^{(\beta-1)}\Phi(\kappa), \tag{1}\]  
_for \(\omega\in\mathbb{N}_{\rho+\beta},\beta>0\)._  
**Definition 2** ([39]).: _For the real-valued \(\Phi:\mathbb{N}_{\rho}\rightarrow\mathbb{R}\), the Caputo difference of arbitrary order \(\beta\) is_  
\[\begin{split}{}^{\mathrm{C}}\Delta_{\rho}^{\beta}\Phi(\omega)=& \Delta_{\rho}^{-(\omega-\beta)}\Delta^{\omega}\Phi(\omega)\\ =&\frac{1}{\Gamma(\omega-\beta)}\sum_{\kappa=\rho} ^{\omega-(\omega-\beta)}(\omega-\kappa-1)^{(\omega-\beta-1)}\\ &\Delta^{\omega}\Phi(\kappa),\end{split} \tag{2}\]  
_where \(\omega=[\beta]+1\), \(\beta>0,\omega\in\mathbb{N}_{\rho+\omega-\beta}\)._  
**Theorem 1** ([40]).: _Consider a \(\beta\)-th-order discrete time system given by_  
\[\begin{split}{}^{\mathrm{C}}\Delta_{\rho}^{\beta}\Phi(\omega)=& \mathcal{G}(\omega+\beta-1,\Phi(\omega+\beta-1)),\\ \Delta^{\omega}\Phi(\rho)=&\Phi_{\omega},r=\lceil \beta\rceil+1,\omega=0,1,2,\ldots,r-1,\end{split} \tag{3}\]  
_then we obtain the numerical form as follows_  
\[\Phi(\omega)=\Phi_{0}(\omega)+\frac{1}{\Gamma(\beta)}\sum_{\kappa=\rho+\sigma -\beta}^{\omega-\beta}(\omega-\kappa+1)^{(\beta-1)}\mathcal{G}(\kappa+\beta-1,\Phi(\kappa+\beta-1)),\ \omega\in\mathbb{N}_{\beta+\sigma}, \tag{4}\]  
_with_  
\[\Phi_{0}(\omega)=\sum_{\omega=0}^{\sigma-1}\frac{(\omega-\rho)^{(\omega)}}{ \Gamma(\omega+1)}\Delta^{\omega}\Phi(\rho). \tag{5}\]  
**Remark 1**.: _Choosing the discrete kernel function_  
\[\sum_{\kappa=\rho+\sigma-\beta}^{\omega-\beta}(\omega-\kappa+1)^{(\beta-1)}\]  
_as \(\frac{\Gamma(\omega-\kappa)}{\Gamma(\beta)\Gamma(\omega-\kappa-\beta+1)}\) with assumption \(\rho=0\) and \(\kappa+\beta=\rho\), the numerical formula when \(\beta\in(0,1)\) is obtained as_  
\[\Phi(\omega)=\Phi(0)+\frac{1}{\Gamma(\beta)}\sum_{\rho=1}^{\omega}\frac{ \Gamma(\omega-\rho+\beta)}{\Gamma(\omega-\rho+1)}\mathcal{G}(\rho-1,\Phi( \rho-1)). \tag{6}\]
## 2 Digital Image Processing
### Crack Extraction  
The crack damage detection method of expressway asphalt pavement based on digital image processing technology mainly extracts cracks through three steps:  
1. Non-fracture image filtering;
2. Intensity image acquisition;
3. Regional information enhancement.  
First, according to the high-frequency characteristics, the relevant algorithm is used to calculate the target high-frequency signal in the pavement image. The image with a weak target high-frequency signal is a crack-free image, and the processing is skipped. Second, for images with possible cracks, relevant extraction algorithms are designed based on the non-negative features and contrast features of the image crack regions to obtain candidate information on cracks. Then, according to the linear characteristics of the image crack region, relevant algorithms are designed to strengthen the breakpoints and weaken the background noise. Finally, the false information is eliminated, and the cracks in the asphalt pavement image of the expressway are extracted. It should be noted that in order to ensure that the crack information in an image can be accurately captured and analyzed, there are certain requirements for the size or resolution of the image. A larger image size may require more computing resources and storage space, but it can also provide more image information for crack detection. Higher resolution can provide more detailed information, which helps to identify and locate cracks in crack detection more accurately. However, higher resolution also increases the cost of computation and storage. Therefore, it is necessary to ensure that the road surface image resolution used for detection is at least 150 dpi.