esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Theoretical Analysis
### Bridge Dynamics of a Simple VBI System
^{-1} \tag{10}\]  
Considering that the inclination and acceleration can be calculated following Equation (11a,b), we obtain  
\[\theta_{low}(x,t)=\frac{du_{low}(x,t)}{dx}=\frac{d\phi_{dis}(x)}{dx}.Y_{dis-low}(t) \tag{11a}\] \[\ddot{u}_{high}(x,t)=\frac{d^{2}u_{high}(x,t)}{dt^{2}}=\phi_{dis}(x).\frac{d^{2}Y _{dis-high}(t)}{dt^{2}} \tag{11b}\]  
The inclination and acceleration mode shape functions can be calculated by substituting Equation (5a) on to Equation (11a,b), respectively, as  
\[\phi_{incl}(x)=cos\frac{n\pi x}{L}\] (12a) and \[\phi_{acc}(x)=sin\frac{n\pi x}{L} \tag{12b}\]  
There are two features that should be noted:  
1. Equation (11a) demonstrates that the inclination mode shape function is the derivative of the displacement mode shape function with respect to \(x\), which should also hold true for continuous beams. Moreover, the time function \(Y_{dis-low}(t)\) is identical for inclination and displacement. 2. Equation (11b) illustrates that the process of differentiating displacement twice to obtain acceleration does not alter the mode shape function. In detail, the inter-section transfer matrices for the inclination and acceleration are  
\[T_{incl}= \begin{bmatrix}cos\frac{\pi\tau_{2}}{L}&cos\frac{2\pi\tau_{2}}{L}\\ cos\frac{\pi\tau_{3}}{L}&cos\frac{2\pi\tau_{3}}{L}\\ \end{bmatrix}\begin{bmatrix}cos\frac{\pi\tau_{1}}{L}&cos\frac{2\pi\tau_{3}}{L} \\ cos\frac{\pi\tau_{2}}{L}&cos\frac{2\pi\tau_{2}}{L}\\ \end{bmatrix} ^{-1}\] (13a) and \[T_{acc}=T_{dis}= \begin{bmatrix}sin\frac{\pi\tau_{2}}{L}&sin\frac{2\pi\tau_{2}}{L }\\ sin\frac{\pi\tau_{3}}{L}&sin\frac{2\pi\tau_{3}}{L}\\ \end{bmatrix}\begin{bmatrix}sin\frac{\pi\tau_{3}}{L}&sin\frac{2\pi\tau_{3}}{L} \\ sin\frac{\pi\tau_{3}}{L}&sin\frac{2\pi\tau_{2}}{L}\\ \end{bmatrix} ^{-1} \tag{13b}\]  
Then, _the first key principle_ can be derived from Equations (12b) and (13b):  
_First key principle:The high-frequency acceleration mode shape function and low-frequency displacement mode shape function are identical. Consequently, transfer matrices for the low-frequency displacement and high-frequency acceleration are identical._
# Converging Telco-Grade Solutions 5G and beyond to Support Production in Industry 4.0
## 5 The Telco Cloud Enabled 5G Applications
### Telco Cloud Industrial Applications
#### 5.5.8 Healthcare  
Obtains real-time information about the patient's condition, such as blood pressure, heart rate, oxygen levels in the blood, and so on [212] by connecting healthcare equipment to the cloud without the need for the patient to be present in the hospital, and the medical reports on patients are updated on a regular basis. There is an optimal hospital resource investment to avoid unnecessary waste and thus save money. Continuous monitoring of the technical condition of medical equipment to avoid unexpected malfunctions [213] is conducted.
## 4 Materials and Methods
### Design of the Introduced CAD
#### 4.2.1 Pap smear Image Preparation  
Pap smear training images were initially augmented to increase the amount of data available in the training procedure. Augmentation is an important procedure that is usually performed to improve the learning procedure of the DL models and prevent overfitting. There are numerous methods for augmentation, in the introduced CAD, flipping, rotation, scaling, and shearing techniques were employed. Next, the dimensions of the augmented images, as well as the original images of the entire Mendeley LBC dataset, were changed to fit the input layers size of the three CNNs which is equal to 224 \(\times\) 224 \(\times\) 3. These CNNs included MobileNet, ShuffleNet, and ResNet-18. CNN models' effectiveness is because of the complexity and intensity of their structure. The quantity of parameters included in the model grows in proportion to the model's complexity [50]. During the learning phase, presented CNN models involved huge amounts of hyperparameter adjustments. Nevertheless, the substantial number of parameters could lower the network's generalization performance and end up causing overfitting [51]. The reduction of parameters and layers by using compact DL models are ways to prevent overfitting caused by the model's complexity [22; 50]. As a result, three compact DL models were utilized in this study.  
MobileNet has been used in this study since it is a compact CNN with fewer parameters and deeper layers that can accomplish accurate results despite being compact. The depth-wise separable convolution [52] is the fundamental basis of the MobileNet layout. The main benefit of depth separable convolution compared to standard convolution is the requirement for less computation time when dealing with huge and complex convolutional networks [53]. Similarly, ShuffleNet is a compact CNN, however, it employs channel shuffle and pointwise group convolution to decrease computation expense while retaining precision. When training ShuffleNet with the ImageNet dataset, it attained less top-1 error than the MobileNet and obtained a 13\(\times\) real increase in speed over AlexNet while  
Figure 2: Introduced CAD’s workflow for cervical cancer diagnosis via pap smear slides.  
preserving similar performance [54]. On the other hand, ResNet-18 basic element is the residual module. These residual blocks are using a skip linkage or \"shortcut\" among every two layers in addition to direct links between all layers. The above enables the network to consider taking activation through one layer and give it into another deep layer in the CNN, thereby enduring the network's learning hyper-parameters in deeper layers.
# A Sparse Algorithm for Computing the DFT Using Its Real Eigenvectors
## 4 Computing Orthonormal Eigenvectors of the DFT
### Gram-Schmidt Orthonormalization  
Numerically similar orthonormal eigenvectors can be found from (18) via the Gram-Schmidt orthonormalization process. As discussed before, the matrices \(P_{e}\) are not full rank. Therefore, we only consider the first \(m_{1}\) and \(m_{-1}\) columns of \(P_{1}\) and \(P_{-1}\); ignore the first column of \(P_{j}\) and \(P_{-j}\) and consider the next \(m_{j}\) and \(m_{-j}\) columns. In our observation, using Gram-Schmidt orthonormalization on the DFT projection matrices gives the fastest and most numerically accurate results when run on MATLAB(r). Accuracy tests and their results are discussed in Section 6. We observed that the eigenvectors obtained using the Matveev method is somewhat sparse. That same sparse structure is retained by the Gram-Schmidt orthonormalization of the DFT projection matrices as well as the Gram-Schmidt orthonormalization of McClellan and Parks eigenvectors.
# Reduction Potential of Gaseous Emissions in European Ports Using Cold Ironing
## 6 Power Needs and Gaseous Emissions in Ports  
An intriguing problem and possibly one of the greatest obstacles to an extensive application of cold ironing is the huge power needs of the ships during their stay in ports. An extensive investigation carried out from various typologies of sources, such as shipbuilders, ship owners, and cold ironing early implementers, allowed identification of the range of power needs per ships' categories summarized in Table 2.  
The next step was the calculation of the energy need depending on the estimations of the time spent in ports and the emission factors of auxiliary engines, e.g., in grams per kilowatt-hour [g/kWh], which are almost consolidated and moderately variable values, assumed from [10] for the present study (Table 3).  
Additionally, available data on the average emission factors from electricity production in the European Union according to the average mix of sources have been collected (Table 4).
# A New In Vitro Model to Evaluate Anti-Adhesive Effect against Fungal Nail Infections
## 2 Materials and Methods
### In Vitro Evaluation of Reducing Fungal Adhesion to the Nail Plate
#### 2.4.3 Microbiological Assessment  
The fungal cultures were prepared in potato dextrose broth for each type of fungal strain with different incubation times (24 h for _C. albicans_ and 72 h for _T. rubrum_). The microbial suspensions had initial inoculums of approximately 2-3 \(\times\) 10\({}^{6}\) CFU/mL (colony-forming unit/mL) for _C. albicans_ and 2-3 \(\times\) 10\({}^{5}\) CFU/mL for _T. rubrum_.  
The treatment groups (1-5) were introduced within the cultures, one for each contact time (15 min, 5 h and 24 h), for both fungal strains. At the end of each contact time, the membranes were extracted, shaken and placed in a Petri dish to dry for a maximum of 20 min at 37 \({}^{\circ}\)C. Then, the membranes were put into 2 mL of sterile water, gently mixed and, after 30 min, three dilutions were made from that solution (1:100, 1:10,000 and 1:1,000,000). The fungal dilutions were collected on SDA and PDA for the different strains. The plates were incubated in aerobic conditions for 24 h at 35-36 \({}^{\circ}\)C (_C. albicans_) and for 72 h at 21-26 \({}^{\circ}\)C (_T. rubrum_).  
After the exposure time, the antiadhesive effect (AE) of the product was calculated by applying the logarithmic reduction rate equation given below:  
\[\text{AE}=\text{log(Nc)}-\text{log(Nd)}\]  
where Nc is the colony-forming units (CFU) measurement of the control membrane suspension (sample 3) and Nd is the colony-forming units (CFU) measurement of the treated sample suspension (after application of groups 1-5). For each microorganism, three replicates were analyzed for each test.  
Sterility and growth controls were also included for each time and fungal strain. The results from all membranes over time were expressed as the mean \(\pm\) SD (standard deviation) of the treatment group. This test was carried out following suitably modified USA and European Standards (EN) applicable for testing the efficacy of disinfect (bactericidal or fungicidal activity) used in food, industrial, domestic and institutional areas [31, 32, 33].
## 3 Spectrum Management Issues
### M-Milmo
In order to create communication, two different spectrum utilization techniques can be used, i.e., Time Division Duplex (TDD) and Frequency Division Duplex (FDD). For the mmWave spectrum, M-MIMO is expected to deliver better results in the TDD mode rather than in FDD mode; for sub-6 GHz, FDD provides remarkable results [215; 216]. In the FDD communication mode, the uplink and downlink transmissions usually occur in the same channel simultaneously, thus suppressing a great deal of the spectrum gain, as shown in Figure 6[217; 218]. The FDD is designed for paired uplink/downlink spectrum channel access, which requires two dedicated channels with constant spacing between them. It involves high channel training overhead [219], resulting in linear growth of the channel budget based on the number of the transmit antenna elements. This drawback makes the FDD mode unattractive for M-MIMO system deployment [220]. Meanwhile, the TDD takes advantage of channel reciprocity because the number of transmit antennas used has no direct influence over the channel overhead [221]. In the TDD mode, the aggregation of unlicensed spectrum for downlink and uplink serves the same purpose as in a typical LTE-A TDD CA, which offers the advantage of flexible coordination of resources between the uplink and downlink [222]. Therefore, the M-MIMO system is expected to function in the TDD mode for maximum spectral efficiency for the current 5G networks [223]. Furthermore, when TDD is operated in the 4.5-5 GHz spectrum for radars running service, the transmission must be coordinated through the dynamic frequency selection and transmit power control mechanisms [224; 225]. M-MIMO has been considered one of the likely ways to enhance spectrum efficiency [226]. Various issues, such as minimizing BER, spectrum sensing, receiver design, and channel modeling, must be addressed for the design of M-MIMO systems. Several studies have been conducted to explore these areas; a few of them are discussed in the next subsections.
# Maximally Deficiency: Treatments and Applications for Adolescents
## 2 Orthopedic Approaches  
Maxillary skeletal expansion using an orthopedic appliance is a relatively conservative procedure designed to correct transverse maxillary deficiencies in children and adolescents. The technique involves the application of mechanical forces with the intraoral appliance to widen the transverse dimension of the maxilla by separating the mid-palatal suture. This process is instrumental in addressing both skeletal and dental issues such as posterior crossbites and dental crowding that arise from insufficient maxillary width [3]. Maxillary expansion with an orthopedic appliance is a critical orthodontic intervention used to address a range of complex dental and craniofacial conditions. It is particularly effective in managing cleft lip and palate by correcting anterior bone deficits and resolving width insufficiencies, which prepares the maxilla for further reconstructive treatments [10]. In patients with skeletal Class III malocclusion, palatal expansion enhances the efficiency of the anterior protraction face mask, facilitating the forward movement of the maxilla and improving alignment [11]. Additionally, the alternately rapid maxillary expansion and constriction (Alt-RAMEC) protocol can be utilized to produce a more pronounced disarticulation of the maxilla than can be obtained using bone-borne maxillary expansion [12, 13]. It also may assist in the correction of developing Class II malocclusion by aiding mandibular reposition, thus achieving the correction of the Class II relationship [14, 15]. For individuals with Obstructive Sleep Apnea (OSA), it expands the nasal passages and pharynx, potentially reducing sleep apnea symptoms and enhancing respiratory function [16, 17]. In addition, palatal expansion can improve the aesthetics of patients with a narrow smile arc by creating a broader, more appealing smile and providing a conservative solution for mild dental crowding by creating slight spaces within the arch, which improves dental aesthetics without the need for extractions [18; 19]. Maxillary expansion protocols can be mainly categorized into two types based on various rates and forces: slow maxillary expansion (SME) and rapid maxillary expansion (RME), as shown in Table 1. Each type employs specialized appliances, tailored to the patient's specific needs and developmental stages [20]. The active components of orthopedic appliances for maxillary expansion are primarily comprised springs, jackscrews or both. Springs typically exert forces in the range of several hundred grams, facilitating slow expansion. Conversely, jackscrews offer versatile adjustment capabilities, allowing for protocols that can achieve slow, semi-rapid, or rapid expansion [21; 22]. Selecting the appropriate technique based on the stage of mid-palatal suture maturation and corresponding dental age is essential to minimize the risk of failure, and effectively enhance facial aesthetics and function [3; 6; 7].
## 2 Approach  
### cWGAN  
GAN is a type of generative model that was proposed in 2014 by [PERSON] [13]. GAN is composed of a generator and a discriminator; both networks compete with each other in an adversarial relationship while learning and generating data. The latent variable used to generate data passes through the generator to generate fake data and is then input into the discriminator along with real data. The discriminator performs the task of distinguishing between fake and real data and obtains discriminator loss and generator loss during the discrimination process. Through the process of minimizing discriminator loss and generator loss, the abilities of the discriminator and generator are improved. The final goal is for the generator's ability to improve to the point where the discriminator can no longer distinguish between fake and real data.  
CGAN (conditional GAN) [14] was developed to address the issue of controlling the noise vector that was unable to be adjusted in traditional GANs. By adding conditions to traditional GANs, it became possible to control the data generation process. It also uses a generator based on pix2 pix, making it a supervised learning technique [15].  
WGAN (Wasserstein GAN) [16] was developed to solve the issues of mode collapse and vanishing gradients that occur in traditional GANs. When using the Wasserstein loss function and gradient penalty regularization, WGAN can further improve the stability and performance of the training process.  
cWGAN is a combination of CGAN and WGAN. cWGAN utilizes Wasserstein loss and applies supervised learning to control the noise vector in the same way as CGAN. As a result, cWGAN can effectively address issues, such as mode collapse, gradient vanishing, and controlling data generation by being able to control the noise vector at the same time [17; 18].  
The structure of the cWGAN used in this study is depicted in Figure 1. Figure 1 illustrates the structure of the 2D cWGAN, where two-dimensional input data are used, and Figure 1 illustrates the structure of the 3D cWGAN, where three-dimensional input data are used. In both models, the input data \(x\), which are a patch of seismic data with missing traces, are fed into the generator. The predicted result \(G(x)\), which is the interpolated data, and the input data \(x\) are simultaneously fed into the discriminator to undergo discrimination, and its output is \(D(x,G(x))\). Additionally, the input data \(x\) and the corresponding target data \(y\) are used for training the discriminator, and its output is \(D(x,y)\). Once the model is trained, only the generator \(G\) is used to conduct the interpolation.
## 3 Experiments
### Experimental Setting  
To fully demonstrate the timeliness and stability of our proposed method, we conduct comparative experiments in the following order. First, we conduct comparative experiments on the CAVE and Pavia University datasets to test the fusion effect and the excellent timeliness of our proposed method. Then, we test the fusion effects of the various fusion methods at downsampling factors of 4, 8, 16, 32, and 64 to verify the stability of our proposed method. Note that the downsampling factor is the number of pixel points per pixel point interval taken when simulating LR-HSI. For example, when the downsampling factor is 4, the size of the LR-HSI simulated from Pavia University data is 93 \(\times\) 64 \(\times\) 64, and when the downsampling factor is 16, the size of the corresponding LR-HSI is 93 \(\times\) 16 \(\times\) 16. The larger the downsampling factor is, the stronger the super-resolution capability of the fusion is. Here, we take the maximum downsampling factor to 64, i.e., to achieve a super-resolution of 64 times the spatial dimension, and the size of the LR-HSI involved in the fusion is only 93 \(\times\) 4 \(\times\) 4 at this time.
# Congestion Control and Traffic Differentiation for Heterogeneous 6 TiSCH Networks in IIoT
## 3 The Proposed Congestion Control Mechanism
### Exchanging the Queue Backlog Information  
Neighbor nodes need to share their queue backlog information, i.e., \(BF(n_{i})\), in order to be aware of the congestion status. To do so in the method we propose, the value of \(BF(n_{i})\) is implicitly embedded  
Figure 3: Example of the thundering herd problem.  
into the RANK field in the DIO message in the RPL standard [6]. Accordingly, we change the definition of the RANK in the DIO message to:  
\[RANK_{new}(n_{i})=\eta\left(H(n_{i})+1\right)+(\eta-1)BF(n_{i}), \tag{11}\]  
where \(\eta\) is a decoding factor to decode the value of \(BF(n_{i})\) (single value) from \(RANK_{new}\) (two values). \(\eta\) can be any positive integer value that keeps \(RANK_{new}\) within its 16 bit boundary [6]. When a neighbor node receives the DIO message of \(n_{i}\), the values of \(BF(n_{i})\) and \(H(n_{i})\) are decoded separately as follows:  
\[\begin{split} BF(n_{i})&=\frac{mod\left(RANK_{new }(n_{i}),\eta\right)}{\eta-1},\\ H(n_{i})&=\left\lfloor\frac{RANK_{new}(n_{i})}{ \eta}\right\rfloor-1,\end{split} \tag{12}\]  
where \(mod\) () is the modulo operation. This way, the queue backlog information is distributed among neighbor nodes without the need to change the DIO message format, which ensures that the proposed scheme is compliant with the standard RPL.
# YOLO-SWD--An Improved Ship Recognition Algorithm for Feature Occlusion Scenarios
###### Abstract  
Ship detection and recognition hold significant application value in both military and civilian domains. With the continuous advancement of deep learning technologies, multi-category ship detection and recognition methods based on deep learning have garnered increasing attention. However, challenges such as feature occlusion caused by interfering objects, cloudy and foggy weather leading to feature loss, and insufficient accuracy in remote sensing imagery persist. This study aims to enhance the accuracy and robustness of ship recognition by improving deep learning-based object detection models, enabling the algorithm to perform ship detection and recognition tasks effectively in feature-occluded scenarios. In this research, we propose a ship detection and recognition algorithm based on YOLOv11. YOLOv11 possesses stronger feature extraction capabilities and its multi-branch structure effectively captures features of targets at different scales. Three improved modules are introduced: the DLKA module enhances the perception of local details and global context through dynamic deformable convolution and large receptive field attention mechanisms; the CKSP module improves the model's ability to extract target boundaries and shapes; and the WTHead enhances the diversity and robustness of feature extraction. Comparative experiments with classical object detection models on visible and SAR datasets, which include a variety of feature occlusion scenarios, show that our proposed model achieved the best results across multiple metrics, specifically, our method achieved a mAP of 83.9%, surpassing the second-best result by 2.7%.  
Keywords:ship detection; ship recognition; deep learning; remote sensing imagery +
Footnote †: journal: Applied sciences