esa-sceva/satcom-chunk-collection huggingface dataset:
# Accelerated Inference of Face Detection under Edge-Cloud Collaboration
## 4 Evaluation
### Acceleration Effect on the Server  
In order to test the effect of the first-layer acceleration of this algorithm, an inference speed test experiment was performed on the server. The server is equipped with two NVIDIA GeForce 3090s, and only one of the GPUs was used for the test. For the test dataset, 19 face images of different sizes with different detection difficulties were selected. Second, a single image was also selected as the test with a resolution of \(3\times 512\times 512\). Finally, the model was converted into a Tensor RT model, which is referred to as the TRT model in the table. For the inference delay of the model without TRT and the model with TRT, 19 face pictures of different sizes were used in the experiment, and the inference delay includes  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Pruning Strategy** & **Compression Ratio** & **AP\_Easy** & **AP\_Medium** & **AP\_Hard** & **FLOPs/GMAC** & **Parameter/M** \\ \hline \multirow{3}{*}{Backbone pruning} & 1 & 83.635 & 79.481 & 79.481 & 40.15 & 15.815 \\  & 0.80 & 83.682 & 79.995 & 56.332 & 56.332 & 11.341 \\  & 0.60 & 81.636 & 77.014 & 53.785 & 33.576 & 7.81 \\ \hline \multirow{3}{*}{Deconvolution pruning} & 1 & 83.635 & 79.481 & 54.748 & 40.15 & 15.815 \\  & 0.80 & 83.709 & 80.031 & 56.518 & 26.96 & 10.145 \\ \cline{1-1}  & 0.60 & 81.965 & 77.983 & 53.812 & 16.331 & 5.777 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of different approaches for CNN inference.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **AP\_Easy** & **AP\_Medium** & **AP\_Hard** & **FLOPs/GMAC** & **Parameter/M** \\ \hline PyramidBox & 92.6 & 92.0 & 86.2 & 236.58 & 57.18 \\ S3 FD & 92.3 & 90.70 & 82.2 & 96.60 & 22.46 \\ SSH & 92.1 & 90.7 & 70.2 & 99.98 & 19.75 \\ Ours & 81.965 & 81.965 & 53.812 & 19.138 & 5.777 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of different models on the WIDER FACE dataset.  
the data processing time of the 19 pictures, which is converted into a unified input size \(3\times 512\times 512\).  
As shown in Table 4, the deconvolution pruning algorithm can speed up the processing time for the reduced model after compression pruning, whether it is a TRT model or a non-TRT model. In the inference delay of a single image, the deconvolution pruning model with a compression rate of 0.6 has an inference speed of only 1.434 milliseconds, which is nearly 41.87% faster than that of the model without any pruning processing. At the same time, the algorithm also reduces model load time. It can be seen that the first step acceleration strategy greatly reduces the inference delay of the model on the server.
# The Role of Extracellular Matrix (ECM) Adhesion Motifs in Functionalised Hydrogels
### Enhancing the Complexity of Biomaterials for Tissue Engineering Applications  
Moving beyond the capability of the physical structure of hydrogels to guide cell behaviour, the importance of using the native 3D microenvironment to influence cell behaviour and function has become paramount in the field of tissue engineering [49]. Research has begun to focus on ways to increase the biocomplexity of tissue engineering scaffolds, through incorporating adhesion sites or sites susceptible to proteolytic degradation [49]. When this notion was first considered, decellularized natural scaffolds were mainly used to investigate cellular behaviour. These matrices were considered to be the gold standard for organ regeneration, as they contained no native cellular components but maintained original ECM architecture and vascularity [50]. However, these materials possessed multiple limitations, whereby purification techniques caused damage to the structure of the native ECM and had considerable batch-to-batch variation in scaffold composition. Immunogenic responses often resulted from the purified materials, limiting their use for in vivo analysis [49]. Consequently, tissue engineers have begun to utilise and develop novel synthetic materials that do not possess the unpredictable variation associated with natural, decellularized scaffolds [49]. These synthetic materials can also be modified to mimic features of the native ECM in a bottom-up approach, where any scaffold is seen as a blank canvas on which scientists can add desirable features, such as motifs for adhesion or migration [51].  
As most synthetic materials lack any natural promotion of cellular behaviours such as adhesion and spreading, chemical modification to promote cell adhesion is the most common biomimetic modification in the design of tissue engineering constructs. In early studies, this was achieved by coating scaffolds with ECM proteins such as fibronectin or laminin, which were known to promote adhesive properties [49]. However, further research has revealed that ECM proteins perform this key cellular behaviour through conserved amino acid sequences such as RGD or IKVAV [51]. This finding has resulted in extensive research being undertaken to develop biomimetic materials using these short peptide sequences [51], rather than the full-length proteins.
## 3 Spacecraft Dynamics
### Governing Equations of Smart Structures
#### 3.2.2 Piezo Patch Sensors  
The sensor device can be simulated using finite elements, similar to the approach used for modeling the passive structure. Specifically, a piezo patch, composed of a single layer of piezoelectric material sandwiched between two electrodes, can generate an electric charge when it experiences structural deformation. This electric charge, denoted as \(Q_{s}\), is collected on the electrodes of the sensor and then processed through a current amplifier. Under the assumption of a Euler-Bernoulli beam and a sensor with a constant width, as demonstrated in [34], the charge can be derived as follows  
\[Q_{s}=-z_{m}d_{31}b_{p}\big{(}w^{\prime}(b)-w\prime(a)\big{)}=m_{pz}\big{(}w^{ \prime}(b)-w^{\prime}(a)\big{)} \tag{22}\]  
where \(z_{m}\) is the patch distance from the passive structure neutral plane, \(d_{31}\) is the piezo electromechanical coefficient, \(b_{p}\) is the sensor width, and \(w^{\prime}\) is the integral of the structural curvature. It is clear that the sensor output is proportional to the difference of slopes (i.e., rotations) at the extremities of the sensor strip, which are here reconstructed via the finite elements structural model. Hence, the local element coupling term reads as  
\[\left(K_{u\varphi}^{s}\right)^{e}=m_{pz}[0,0,0,0,0,-1,0,0,0,0,0,1] \tag{23}\]  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Model** & **P-842.40** \\ \hline Material & PIC255 \\ Density \(\left(\text{kg}/\text{m}^{3}\right)\) & 7800 \\ Mass \(\left(\text{g}\right)\) & 64 \\ Length \(\left(\text{mm}\right)\) & 91 \\ Electro-mechanical coupling coefficient \(d_{33}(V/m)\) & \(400\times 10^{-12}\) \\ Dielectric coefficients \(\chi_{33}\) & \(1.637\times 10^{-8}\) \\ Elastic compliance constant \(\text{s}_{33}\) & \(1.1\times 10^{-10}\) \\ Voltage range \((V)\) & [0–100] \\ \hline \hline \end{tabular}
\end{table}
Table 4: Piezo-stack actuator properties (from PI manufacturer catalogues).  
while the capacitance matrix of the element [34] is given as follows  
\[\left(\mathbf{K}_{\varphi\varphi}^{a}\right)^{e}=-\frac{A_{pz}\chi_{33}}{t_{pz}} \tag{24}\]  
with \(t_{pz}\) thickness of the sensor. The properties of the implemented sensor patch are reported in Table 5. The implementation from local element to global reference system can be carried out as described in Section 3.2.1.
## 3 Model Comparisons
### Comparison Criteria and Parameter Estimation Method
#### 3.1.2 Criteria for Models' Predictive Power Comparison  
Here we use SSE criterion to examine the predictive power of SRGMs. SSE is the sum of squared error, which is expressed as follows:  
\[\text{SSE}=\sum\limits_{i=m}^{n}(y_{i}-\hat{m}(t_{i}))^{2} \tag{14}\]  
Assume that by the end of testing time \(t_{n}\), totally \(y_{n}\) faults have been detected. Firstly we use the data points up to time \(t_{m-1}(t_{m-1}{<}t_{n})\) to estimate the parameters of \(m(t)\), then substituting the estimated parameters in the mean value function yields the prediction value of the cumulative fault number \(\hat{m}(t_{m})\) by \(t_{m}(t_{m}{<}t_{n})\), \(y_{m}\) is the actual number of faults detected by \(t_{m}\). Then the procedure is repeated for several values of \(t_{i}\) (\(i=m+1\),\(m+2\),...,\(n\),) until \(t_{n}\).  
Therefore, the less SSE, the better is the model's performance.
# Evaluation of Radio Communication Links of 4G Systems
###### Abstract  
Radio communication systems are very widely present in our current smart lifestyles. It consists of two ends, which can carry the transmitter's information to the receptor. Before installing any radio communication system, it is necessary to analyze the link resources. Hence, this analysis allows the determination of the received radio communication strength to prove if it is sufficient for the link to work correctly and assure a high quality of service. For this reason, new services and technologies are integrated. The objective of the present work is to improve the performance of the radio communication link of 4G systems. The study is based on real measurements using the drive test. The data collected by the drive test are analyzed to increase the performance of the radio communication. Based on this data analysis, recommendations and suggestions are  
Okumura-Hata, Ericsson 9999, Lee propagation, and ECC-33 models are used in [4] for the urban traffic noise monitoring system. The path loss models used to evaluate the study in the paper [5] are Okumura-Hata models, COST231 Hata models, IEEE 802.11 ah models, and COST231 Walfisch-Ikegami model. In [6], the path losses calculation is based on the Okumura-Hata model.  
In [7], to obtain the relationship between the distance and the measurement, the authors used multivariable fingerprints to study the path-loss model.  
Other models, close-in, floating-intercept, and alpha-beta-gamma models, are used in [8]. These models are used also in [9; 10; 11]. The procedures of these models are presented in these two papers [8; 9; 10]. The path loss model in [12] is based on the received signal strength indicator measurements. It is a Bayesian particle filter used for overcomes the noise caused by obstacles and sudden signal fading. The power of pure chirp signals from a long-range end-devices radio is applied in [13] to avoid missing information and inaccurate path loss exponent values. It is a case study of point-to-point Internet of Things radio networks measurement.  
For the mobile operators, they use most of these propagation models.  
This work aims to study and evaluate the radio communication links of 4G LTE systems. Based on the analysis of measurement data, the performances of the radio communication are ameliorated by planning and dimensional mobile network on the base of real measurements of the different data. For this purpose, this work is divided into three parts as follows: firstly, the theoretical background on the basic concepts of radio communication links is introduced. The second part introduces the methodology and material used. The results and discussion are presented in the third part. Finally, the paper is concluded by the conclusion.
###### Abstract  
Many time-series smoothing methods can be used for reducing noise and extracting plant phenological parameters from remotely-sensed data, but there is still no conclusive evidence in favor of one method over others. Here we use moderate-resolution imaging spectroradiometer (MODIS) derived normalized difference vegetation index (NDVI) to investigate five smoothing methods: Savitzky-Golay fitting (SG), locally weighted regression scatterplot smoothing (LO), spline smoothing (SP), asymmetric Gaussian function fitting (AG), and double logistic function fitting (DL). We use ground tower measured NDVI (10 sites) and gross primary productivity (GPP, 4 sites) to evaluate the smoothed satellite-derived NDVI time-series, and elevation data to evaluate phenology parameters derived from smoothed NDVI. The results indicate that all smoothing methods can reduce noise and improve signal quality, but that no single method always performs better than others. Overall, the local filtering methods (SG and LO) can generate very accurate results if smoothing parameters are optimally calibrated. If local calibration cannot be performed, cross validation is a way to automatically determine the smoothing parameter. However, this method may in some cases generate poor fits, and when calibration is not possible the function fitting methods (AG and DL) provide the most robust description of the seasonal dynamics.  
Keywords:normalized difference vegetation index (NDVI); smoothing methods; gross primary production (GPP); phenology; TIMESAT; MODIS +
Footnote †: journal: Remote Sensing, 9, 1271; doi:10.3390/rs9121271
## 4 Discussion
In the current study, administration of CV or SeNPs-CV increased the levels of IL-1\(\beta\) in MCF-7 cells. These findings proved that SeNPs-CV or CV might stimulate apoptosis in MCF-7 cells through the activation of IL-1\(\beta\) synthesis. This increment in IL-1\(\beta\) may be due to the oxidative effect of CV and SeNPs-CV, where a crosstalk between oxidative stress and inflammation has been demonstrated, as ROS trigger the activation of NF-\(\kappa\)B, which in turn regulates the production of several inflammatory mediators, including IL-1\(\beta\)[44]. These findings are consistent with prior studies showing that IL-1\(\beta\) promotes apoptosis in pancreatic RIm5F cells via stimulating nitric oxide generation [45] and ceramide synthesis in EL4 thymoma cells [46]. Additionally, [PERSON] et al. [43] indicated that endogenously produced mature IL-1\(\beta\) is directly involved in cell death through the activation of the IL-1\(\beta\) converting enzyme (ICE), which plays a direct role in mediating the apoptotic cascade. Proliferating cell nuclear antigen (PCNA) is termed the \"ringmaster of the genome\" as it regulates the cell cycle and participates in DNA synthesis. So, it is strongly related to prognosis and survival in most types of solid malignancies, such as BC, suggesting an active role of PCNA in tumorigenesis [47]. As a result, PCNA is widely utilized as a marker of cell proliferation in both healthy and malignant tissues since it generally represents cell proliferation activity [48]. Similarly, [PERSON] et al. [49] discovered that PCNA protein levels were higher in BC patients in clinical stages III-IV and lymph node metastasis. Interestingly, in the present study, the administration of CV or SeNPs-CV considerably reduced PCNA expression rates in MCF-7 cells. These findings clearly show the role of CV and SeNPs-CV in the inhibition of tumorigenesis and proliferation in BC through the inhibition of PCNA gene expression. In agreement with our findings, Se treatment arrested BC cell lines in the G1 phase of the cell cycle and inhibited the expression of PCNA genes while inducing the expressions of certain caspase genes [50]. On the other hand, supplementation with CV had an antimetastatic impact on liver cancer by drastically lowering PCNA expression and preventing the local spread of cancerous cells [51]. Further supporting the advantages of SeNPs-CV versus CV, our results demonstrated a significant decrease in PCNA expression rates in the combination group compared to the only-CV group.
# Data Driven Approach for Eye Disease Classification with Machine Learning
## 1 Introduction  
Artificial intelligence (AI) plays an important role in assisting medical experts with early disease diagnosis. There are a large number of AI-based disease detection and classification systems combining medical test results and domain knowledge [1; 2; 3; 4; 5; 6]. However, correlating the actual symptoms and clinical observations with the corresponding diseases is missing in most of these systems. This is perhaps owing to the variety of observation recording methods by medical experts. For example, some use symbols for diagnosis, whereas others give a textual description; hence, there is no standard method. Therefore, this data should be manually converted into a standard format so that machines can use it for analysis. This limits the size of data used in any analytical study, which is the main cause of current gaps in human-knowledge-based diagnosis and machine-intelligence-based predictions.  
Commonly, ophthalmic diseases are not life threatening; however, progress over time can have significant impact on the patient's life. Physical examinations are performed using ophthalmological instruments and a comprehensive interpretation is used for diagnosis. Therefore, any machine-based solution should concurrently consider observations, symptoms and standardized test results forpredictions. Furthermore, the use of a standard description for clinical data and medical test results can be the key to success. The first step toward this is the use of health records in electronic form. Maintaining patient information as digital data has several potential benefits, including rapid retrieval along with timely data transmission among multiple medical experts [7]. Moreover, the use of standard taxonomies for patient data recording can further improve its quality, accuracy and consistency.  
This study focuses on developing a general framework for the standardized recording of patient symptoms and clinical observations, thus assisting medical experts in keeping up with the exponential development of medical knowledge arising from clinical trials and logical advancements in the field. Similarly, medical cases solved in the past may greatly contribute to the training of machine-learning agents for accurate diagnosis [8]. This is also important because machine-learning algorithms can analyze the large number of features required for diagnosis more effectively than humans. Accordingly, intelligent agents, using a carefully designed multi-agent-based classification model, can outperform humans by efficiently analyzing all the features along with previous information [9].  
The rest of the paper is organized as follows. In Section 2, existing machine-based solutions for medical diagnosis are briefly described. In Section 3, the proposed methods for data modeling and pre-processing are explained, as well as the framework designed for analyzing and predicting eye diseases. In Section 4, the results are described and the paper is concluded in Section 5.
# From Security Frameworks to Sustainable Municipal Cybersecurity Capabilities
## 2 Background and Literature
### Municipal Cybersecurity  
Research on municipal cybersecurity has been sparse but has seen an uptick since 2018, driven by the increasing digitalization of municipal services and the move towards smart cities [10; 13]. Studies have identified a gap between the growing recognition of the importance of cybersecurity and the actual implementation of necessary security measures [14], as well as gaps in local government cybersecurity policies [15]. Cyberattacks have serious economic implications for municipalities, and while new regulations address the issue, their effect is disputed [16]. Municipalities struggle with limited economic resources, prioritizing cybersecurity in competition with other pressing needs, recruiting qualified cybersecurity personnel, and providing necessary training and education on cybersecurity [13; 17].  
According to statistics from Statistics Norway (SSB) [18] in 2024, 34.8% of Norwegian municipalities report having outsourced most or all of their security monitoring to external parties. This likely includes many or most of the 26.5% of municipalities that have outsourced their ICT operations to external parties. The numbers do not specify if the external party is an IKS (shared, publicly owned municipal service provider) or a market-based service provider.  
Statistics from the Statistics Norway (SSB) [19] also highlight how municipalities struggle to transform high-level security requirements from cybersecurity frameworks into operative capabilities. For example, while 72% report having a written security policy approved by management, only 61% have centralized logging, 60% have systems for detecting unwanted traffic, and only 31% report performing regular penetration tests. These numbers indicate that municipalities are struggling to translate regulations and security frameworks into the necessary organizational structures with sufficient resources, technological support, skills, and knowledge to perform all the required activities prescribed in the security frameworks.
## 3 Results and Discussion  
### Density  
The measurement of density was performed on the Ultem 9085 in the form of both filament and printed components. The results are reported in Table 1.  
The density of the filament was found to be equal to \(1.2864\pm 0.0005\) g/cm\({}^{3}\); this value was taken as reference to calculate the relative density of printed samples as well as the porosity values.  
From Table 1 it is evident that samples showed increasing relative density in the printing directions XY \(<\) XZ \(<\) ZX; as expected, the corresponding values of calculated porosity showed a reverse trend. This trend of density variation when different printing directions are considered agrees with that observed by [PERSON] et al. [25]. However, these authors reported values of density in the range from 89.6% to 92.1%, therefore quite a lot lower than those presently obtained.  
The variation of density observed for the different building orientations is mainly influenced by the ratio between the extent of the contour and the infill zones, and the scanning strategy adopted to fill each layer. The extruder head firstly deposits the contour of the object outlining its perimeter; then, it completes the layer by depositing the filament within the layer contour with an orientation of \(+45^{\circ}\)/\(-45^{\circ}\). The misalignment of the infill with respect to the contour causes the formation of voids--in fact, when the \(+45^{\circ}\)/\(-45^{\circ}\) filament meets the contour and goes back towards the opposite side, an empty space is left at the corner of infill filament. The higher the infill area involved in the formation of each layer, the higher the fraction of formed voids.