esa-sceva/satcom-chunk-collection huggingface dataset:
# A Cost-Optimized Model for Dynamic Port Hinterland
## 4 Experimental Design and Results
### Results: Representing the \(H(x,y)|_{(p_{n},\rho_{n})}\)
#### 4.2.6 Quantitative Highlight  
Port influence analysis across Spanish urban centers reveals a complex landscape of competition, challenging traditional assumptions about geographic proximity. The study demonstrates that port selection is determined by a sophisticated interplay of factors beyond mere distance.  
Key findings include geographic proximity is not the sole determinant of port influence; infrastructure quality, particularly rail connectivity, critically extends ports' economic reach; market concentration varies significantly by region; and most urban centers benefit from multi-port competition. Notably, Madrid exemplifies how efficient inland connections can enable ports to dominate markets beyond their immediate geographic boundaries.  
The analysis reveals that product characteristics substantially influence hinterland boundaries, with different cargo types revealing distinct competitive patterns. For instance, the supply chains of perishable goods like bananas and oranges demonstrate varied port selection dynamics. This comprehensive research confirms that modern port hinterlands are shaped by a nuanced combination of transportation infrastructure, cost efficiency, and product-specific requirements rather than simplistic geographic considerations.  
Infrastructure emerges as a pivotal factor, with rail networks creating unexpected patterns of port influence. Valencia's performance in distant markets, Barcelona's competitiveness in northern regions, and Algeciras's surprising reach in Galicia--particularly  
Figure 5: Hinterland in Spain: Statistical approximation of integral hinterland with the 4 routes of the example. Note that the color is used in an RGB value, using the color assigned to each port.  
where rail transportation compensates for geographical disadvantages--underscore the complex nature of port competition in Spain.
# Hybridizing Fuzzy String Matching and Machine Learning for Improved Ontology Alignment
## 4 Proposed Method  
Our proposed method employs a suite of fuzzy string-matching algorithms to facilitate the alignment of ontologies at the lexical level. Specifically, we utilized the Jaccard, Jaro-Winkler, Levenshtein, and LCS algorithms, which have been extensively validated in the literature for their effectiveness in this context. While the Word2 Vec algorithm has shown promise in some applications, we found it to be unsuitable due to its inability to handle strings with multiple tokens, as attested by [PERSON] et al. [20]. In addition to these lexical matching algorithms, we incorporate BERT, a powerful language model that leverages contextual information to tackle the semantic aspect of ontology alignment. To combine the similarity scores obtained from these algorithms, we use three regression classifiers as depicted in Figure 2. This enables us to fully leverage the strengths of each algorithm while mitigating their weaknesses, resulting in a more accurate and comprehensive alignment of ontologies. Figure 2 displays the workflow of our proposed method for ontologies alignment using a combination of lexical and semantic similarity metrics. The method begins by extracting labels from the source and target ontologies (\(O_{s}\) and \(O_{t}\)), resulting in two sets of labels, \(O_{s}\)_Labels_ and \(O_{t}\)_Labels_, respectively. Next, we apply a suite of fuzzy string-matching algorithms recursively to align each pair of labels in the two sets. For each fuzzy string-matching algorithm, the similarity scores are computed and stored in a separate text document. This produces a set of similarity scores for each pair of aligned labels, that capture the lexical similarity between them. To incorporate semantic information into the alignment process, we use BERT version 2.2.2 available in the \"sentence-transformers\" Python library package to compute the semantic similarity scores between every pair of aligned labels in \(O_{s}\)_Labels_ and \(O_{t}\)_Labels_. These scores are stored in a separate text document,  
\(SC_{BERT}\), which captures the semantic similarity between the aligned labels. Finally, we combine the lexical and semantic similarity scores using three regression classifiers, namely, kNN, SVR, and DTR. Each classifier produces a final similarity score for each pair of aligned labels, which summarizes the overall similarity score considering both the lexical and semantic features of the input ontologies \(O_{s}\) and \(O_{l}\). Algorithm 1 presents a detailed breakdown of the implementation of the proposed method shown in Figure 2. Table 1 provides a summary of the variables used in Algorithm 1 and their respective descriptions. To evaluate the effectiveness of our proposed method, we compared it to the widely-used AML system [31] version 3.2 as a baseline for aligning two ontologies. In this ex
###### Abstract  
Often, solar power parks are built in remote areas where the provider's internet connection using the GSM network is insufficient. In addition, in order to monitor the parameters of each solar module, a very dense arrangement of IoT components would be required, as a result of which the speed of the external network would not be sufficient. In solving this problem, a hybrid data transmission network (there are no universal hybrid network concepts proposed in the literature for monitoring the operating parameters of a solar power park) was designed and tested for the monitoring system of the working parameters (voltage, current, etc.) of the solar power plant. This network consists of RF modules, RS458 modules communicating with a twisted pair cable, a GSM module, etc. PIC series microcontrollers with original programming code written in the C language were used to ensure the operation of the monitoring system and the hybrid data network. The data of the operating parameters of the solar power plant are stored centrally on a server, in a MySQL database. The central server has the ability to display data not only on the graphical output device, but also to transmit it to mobile devices using the Android operating system. During the testing of this network, observations of the working parameters of the 870 W solar power plant were made. Observations lead to the conclusion that such a network can be fully used in solar power parks as an alternative to the GSM network. Analyzing the data, it was found that the efficiency of bifacial type batteries is about 15-20% higher than that of traditional, silicon-based solar batteries. The voltammetric characteristics of a bifacial type battery, a new silicon battery and a 10-year-old battery basically match, which means that the silicon active material of all types of batteries has a similar alloying degree and the same internal resistance.  
monitoring; artificial intelligence; microcontroller; data; network +
Footnote †: journal: Applied Sciences
# Fast Point Cloud Registration Method with Incorporation of RGB Image Information
###### Abstract  
Point cloud registration has a wide range of applications in 3D reconstruction, pose estimation, intelligent driving, heritage conservation, and digital cities. The traditional iterative closest point (ICP) algorithm has strong dependence on the initial position, poor robustness, and low timeliness. To address the above issues, a fast point cloud registration method that incorporates RGB image information is proposed. The SIFT algorithm is used to detect feature points of point clouds corresponding to the RGB image, followed by feature point matching. The RANSAC algorithm is applied to remove erroneous point pairs in order to calculate the initial transformation matrix. After applying a pass-through filter for noise reduction and transiting down with a voxel grid, the point cloud is subjected to rotation and translation transformation for initial registration. On the basis of initial alignment, the FR-ICP algorithm is utilized for achieving precise registration. This method not only avoids the problem of ICP easily getting stuck in local optima, but also has higher registration accuracy and efficiency. Experimental studies were conducted based on point clouds of automotive parts collected in real scenes, and the results showed that the proposed method has a registration error of only 0.487 mm. Among the same group of experimental point clouds with comparable registration error, the proposed method showed a speed improvement of 69%/48% compared to ICP/FR-ICP with regard to registration speed.  
iterative closest point (ICP); point cloud registration; SIFT feature extraction; RANSAC; precise registration +
Footnote †: journal: Applied sciences
# Zeroize: A New Method to Improve the Utilization of 5G Networks When Running VoIP over IPv6
## 2 Related Work
One of the recently considered solutions is to utilize the header's superfluous fields to carry the voice data of the packet. This solution is based on the assumption that the VoIP packet header protocol transfers various types of application data. Therefore, some of the fields in these protocols are necessary with some applications, but they are not with some other applications. The latter are burdening the bandwidth without valuable use. These fields are called the superfluous fields [14; 15]. This last solution to the bandwidth problem uses the superfluous fields to carry the voice data of the packet, thereby reducing or eliminating the payload and saving bandwidth. One of the best methods under this solution was proposed by [PERSON] et al., and it is named short voice frame (SVF). The SVF method successfully utilizes the seven superfluous fields of the packet header on the basis of certain assumptions. These fields manage to save 17 bytes of each VoIP packet, which is typically between 50 and 70 bytes. The analysis results proved that the saved bandwidth can reach around 29% in some instances [14]. Another method called zero size payload (ZSP) is developed to utilize superfluous fields. The ZSP method uses different assumptions compared with the SVF method. Therefore, it manages to utilize the 10 superfluous fields with 19 bytes. The analysis results confirmed that the saved bandwidth can reach around 32% in some instances [15]. Developers have exerted great efforts to save bandwidth when running VoIP. The bandwidth issue becomes difficult when integrating the VoIP and IPv6 protocol, owing to the large size of the IPv6 header. However, the bandwidth problem of the VoIP and IPv6 protocol is not addressed, especially with 5G wireless networks. In this study, a new method is designed to solve this problem using the superfluous fields in the VoIP packet header. The proposed method uses the source IPv6 address to carry the digital voice data of the VoIP packet. This process minimizes the VoIP packet payload size or makes it zero; thus, the designed method is called the Zeroize (zero sizes) method. The Zeroize method enhances bandwidth utilization and improves the quality of the calls.
# Fall Recognition System to Determine the Point of No Return in Real-Time
## 3 Data Acquisition
### Composition of the Falls Data Acquisition System  
The fall data acquisition system that was developed to collect data in this study consists of a data capture device, a real-time data processing S/W program, a visual tool for previewing and editing the obtained data. Commercial data capture devices generally obtain motion data from the entire body using 17 IMU sensors. A single sensor can collect data at a rapid interval of 100 Hz. However, the capture period increased according to the volume of data to be transferred when all sensors were utilized. In addition, real-time data cannot be directly used in some cases because only data stored by a special S/W program can be used. Thus, an independent data capture device was developed in this study to obtain data in real-time. The device comprised five IMU sensors that were connected in the form of a daisy chain USB hub, enabling users to attach these sensors to their bodies more conveniently. The real-time data processing S/W program based on the server-client structure managed data collected from the five sensors in the form of packets and transmitted them through wired and wireless network communication. The visual tool for previewing and editing the obtained data prevented the collection of incorrect data by analyzing the data obtained by the data capture device, identifying motion based on skeleton-shaped images and facilitating label editing in preparation for a context that necessitates manual labeling.  
The fall data acquisition system was operated based on the following processes: first, the IMU sensors of the data capture device output Quaternion, Euler, accelerometer, gyroscope, and magnetic data at 100 Hz. The real-time data processing S/W program received the aforementioned data and transferred them to the visual tool through wired and wireless networks. The visual tool monitored data in real-time and stored them as CVS files based on the preview function. Label editing was performed when needed.
# A Novel Binary Competitive Swarm Optimizer for Power System Unit Commitment
## 2 Problem Formulation
### Objective Function
#### 2.1.2 Start-Up Cost  
Due to the running time limit and other restrictions, the unit start-up requires an extra cost of the fuel. The cost of start-up is therefore an indispensable part for the total economic cost. It is described by Formula (3):  
\[SU_{j,t}=\left\{\begin{array}{l l}SU_{H,j},&if\qquad MDT_{j}\leq TOFF_{j,t} \leq MDT_{j}+T_{cold,j}\\ SU_{C,j},&if\qquad TOF_{j,t}>MDT_{j}+T_{cold,j}\end{array}\right\} \tag{3}\]  
The start-up cost of a unit may be categorized as hot start and cold start. When a unit has been turned off for a short period of time, it is calculated as hot start. Otherwise, cold start cost may have to be taken into account. Therefore, the start-up cost is closely related to the previous running status and current status of the unit. In this section, \(SU_{H,j}\) indicates the hot start-up cost of the \(j\)th unit, and the \(SU_{C,j}\) denotes the cold start-up cost of the \(j\)th unit. \(T_{cold,j}\) is the cold start hour of the \(j\)th unit. \(MDT_{j}\) denotes the minimum down time of \(j\)th unit, and \(MUT_{j}\) denotes the minimum up time of \(j\)th unit. \(TOFF_{j,t}\) represents the continuous time of the \(j\)th unit with off status.
###### Abstract  
In this work, we removed copper (II) from an aqueous solution by using zeolite modified with a silicon-organic monomer (3-aminopropyltriethoxysilane; APTES) depending on the pH, time, temperature, and initial concentration of Cu(II) ions. To confirm the modification process and assess the interaction between the modified zeolite and Cu(II), we performed instrumental analyses (XRD, SEM/EDX, TGA/DTA, BET, FT-IR, and XPS). We determined the maximum adsorption capacities of the modified zeolite for Cu(II) to be 4.50, 6.244, 6.96, and 20.66 mg/g at \(T=25\)\({}^{\circ}\)C (pH = 5, \(t=8\) h) when the initial concentrations of Cu(II) were 50, 100, 200, and 400 mg/L, respectively. According to the adsorption thermodynamics and kinetics, the second-order reaction controls the adsorption process. Based on the two isotherm models (Langmuir and Freundlich) with constant values (\(K_{L}=0.144\), \(n=2.764\)) and the correlation coefficients (\(R^{2}=0.8946\), \(R^{2}=0.9216\)), we concluded that the Cu(II) adsorption onto the modified zeolite could be followed by the Freundlich isotherm model rather than the Langmuir isotherm model. The modified zeolite could be an effective material for the removal of Cu(II) from aqueous solutions.  
adsorption; APTES (3-aminopropyltriethoxysilane); isotherms; heavy metal +
Footnote †: journal: Applied Sci. 2022, 12, 11344. [[https://doi.org/10.3390/app122211344](https://doi.org/10.3390/app122211344)]([https://doi.org/10.3390/app122211344](https://doi.org/10.3390/app122211344))
# The Role of Fucoidan in Controlling _Listeria monocytogenes_ Biofilms on Seafood-Contact Surfaces
## 3 Results
### Relative Expression Levels  
Figure 4 illustrates the downregulation levels of virulence and stress response genes (_flaA_, _fbp_, _prfA_, _hlyA_, and _agrA_), measured by RT-qPCR, in response to sub-inhibitory concentrations of fucoidan (ranging from 0 to 125 \(\upmu\)g/mL). Gene expression was significantly downregulated at different concentrations of fucoidan (\(p\) < 0.05). The fold changes in gene expression for _flaA_, _fbp_, _prfA_, _hlyA_, and _agrA_ at control, 1/8, 1/4, and 1/2 MIC were as follows: _flaA_--1.00, 0.864, 0.537, 0.308; _fbp_--1.00, 0.850, 0.475, 0.321; _prfA_--1.00, 0.825, 0.546, 0.364; _hlyA_--1.00, 0.824, 0.524, 0.364; _agrA_--1.00, 0.856, 0.570, 0.351. These results indicate that fucoidan treatment affects the demonstration of key genes associated with bacterial pathogenicity and QS in _L. monocytogenes_.  
Figure 4: Downregulation levels of _agrA_, _prfA_, _fbp_, _flaA_, and _hlyA_ genes in _Listeria monocytogenes_ culture under control, 1/8 MIC, 1/4 MIC, and 1/2 MIC quercetin treatments. Data represent the mean of three replicates (\(n\) = 3). Different letters (a–c) indicate significant differences (\(p\) < 0.05).  
Figure 3: The data shows the dose-dependent effect of fucoidan in reducing the biofilm of _Listeria monocytogenes_ on the SS surface. Results are stated as mean \(\pm\) SEM (\(n\) = 3), with statistically significant differences indicated by different letters (a–c) at \(p\) < 0.05.
## 2 Methods
### Special Telecommunications Services  
The measurements were performed in a TEMPEST laboratory consisting of two shielded enclosures that have a common wall, as illustrated in Figure 3. One of the enclosures is a semi-anechoic chamber (there is no radio absorbent material on the floor) and is actually the testing room. The equipment is positioned in accordance to the American EMC standard, MIL-STD-461E. This standard has many similarities, but also some differences with the test configuration specified by the TEMPEST procedures, but in the end it has the closest configuration of all EMC standards. As TEMPEST procedures are classified, information cannot be disclosed, so we will consider the configuration of this EMC standard as a reference.  
Figure 2: Communication Institute test bed.  
The tests were performed with a Fujitsu Siemens laptop, Lifebook C110. The chosen equipment is an older generation laptop and was used with the 220V mains power supply unit. The laptop's electric charger is usually an electromagnetic noise generator that makes the process of video image recovery more difficult to fulfill. Very often, the emissions of these electric chargers are in the frequency range of up to about 500 MHz. For this reason we selected a much higher recovery frequency, respectively 910 MHz.  
It does not matter which EUT is chosen for testing, but to do comparative tests, we also used, besides the analyzed fonts, 2 other common fonts, namely Times New Roman and Arial.  
It should be mentioned that, after establishing the reception and the video recovery parameters, only the images displayed by the EUT have been modified, while all the reception parameters and the analysis of the compromising signal remained unchanged until the end of the tests performed. We used a Rohde & Schwarz (R&S) FSET 22 test receiver and specialized TEMPEST analysis software. Unfortunately, we had to crop from the analyzed images the information that includes the video recovery parameters, because it represents sensitive information that we are not allowed to publish. The test receiver parameters are: 10 MHz Resolution Bandwidth (RBW), 50 ms sweep time, SPAN 0 working mode, and narrow filter pre-selector, and internal preamp amplification was 10 dB. The display resolution of the EUT is \(1024\times 768\) with the refresh rate of 60 Hz. Thus, all the time parameters of the displayed video signal can be found in the VESA Coordinated Video Timings (CVT) Standard, version 1.2 (February 2013): vertical refresh of approximately 48.363 kHz (most important), pixel frequency of 65.0 MHz, front and back porches (horizontally and vertically), the whole line and whole frame duration.  
Another interesting discussion would be the size of the font that would be most suitable for use in our tests. Usually documents are edited with size 12, while PowerPoint presentations use size 28 or even larger to be visible to all participants. So, we used the following dimensions: 36, 28, 24, 20, 18, 14 and 12.
# CDTNet: Improved Image Classification Method Using Standard, Dilated and Transposed Convolutions
## 5 Conclusions  
In this study, we proposed CDTNet with standard, dilated, and transposed convolutions. The standard and dilated convolution can extract multi-scale features, and the  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **VGG16** & **VGG19** & **ResNeXt [46]** & **DenseNet [9]** & **CDT\_C** & **CDT\_A** \\ \hline Parameters (M) & 32.06 & 37.13 & 23.84 & 27.2 & 24.18 & 16.79 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Parameters of CDTNet and classical models.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Models** & **VGG16** & **VGG19** & **ResNeXt** & **DenseNet** & **CDT\_C** & **CDT\_A** \\ \hline Accuracy & 0.9213 & 0.9207 & 0.9172 & 0.9133 & 0.9337 & 0.9331 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Accuracies of CDTNet and other models on FMNIST.  
Figure 9: Compared results on FMNIST.  
transposed convolution can transmit features from low level to high level, which can recover part of the lost information in the pooling layers. Because the object size is small, we used a dilated rate of 2 to fetch the features to concatenate the output of standard convolution. Each block except block 1 was followed by a transposed operation to increase the spatial size of the feature maps to recover low-resolution prediction maps.  
We evaluated the model on CIFAR-10, SVHN, and FMNIST datasets with VGG16, VGG19, ResNeXt, and DenseNet. CDTNet improves the average test accuracy by 1.48% to 20.13% and reduces average test loss by 14.1% to 56.6% on CIFAR-10. On SVHN, CDTNet improves the average test accuracy by 1.59% to 54.81% and reduces the average test loss by 15.51% to 62.85%. On FMNIST, CDTNet improves the average test accuracy by 1.28% to 2.23%. The experimental results show that all evaluation metrics of CDTNet are better than those of the state-of-the-art models, which proves that CDTNet has better performance and strong generalization abilities--and fewer parameters.  
In future work, we will explore more effective architecture to fuse different granularity features and adopt diversified evaluation metrics to analyze the performance. In addition, as not all input image sizes are to the nth power of 2, in future work, we will explore a more effective method to set the number of TC channels and design the feature size after TC operation.  
Conceptualization, H.C. and Y.L.; methodology, Y.Z.; software, X.L.; validation, Y.Z., H.C., and Y.L.; formal analysis, H.C.; writing--original draft preparation, Y.Z.; writing--review and editing, X.L.; visualization, X.L.; supervision, H.C.; project administration, Y.L.; funding acquisition, H.C. and Y.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Basic and Applied Basic Research Fund of Guangdong Province, grant number 2019B1515120085.  
Not applicable.  
The authors declare no conflict of interest.