esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Method: Massive MIMO Network Design
### Problem Description
#### 3.1.3 Bi-Objective Fitness Function  
The bi-objective fitness function accounts for the power consumption and the global EMF exposure. The latter combines the effects of both the downlink and uplink EMF exposure in terms of the total dose, which is the sum of the downlink dose due to the transmission of the BSs and the uplink dose resulting from the user's devices. These fitness functions are defined as follows:  
1. Power Consumption fitness function Here, the fitness function \(f_{1}^{{}^{\prime}}\) that addresses the power consumption objective is similar to Equation (6).
2. Dose (EMF) fitness function The dose fitness function accounts for the optimization of both the downlink and uplink exposure through a global metric as follows: \[f_{2}^{{}^{\prime}2}=\min_{\{y,p\}} \left(1-\frac{\frac{dose\left(y_{k}p_{k}\right)}{dose_{\text{max}}}}{\text{ dose}_{\text{max}}}\right)\] (18)With \(\text{dose}(\ )\ (\frac{W\cdot\text{s}}{\text{Kg}})\) the total dose due to the designed network and \(\text{dose}_{\text{max}}(\frac{W\cdot\text{s}}{\text{Kg}})\) the total dose due to the entire network when assuming all the BSs are active and working with the maximum antenna array configuration (256 antenna elements transmitting at max power and all devices active in UL with max power). The _dose_ is defined by the expression below:  
\[dose=d^{UL}+d^{DL} \tag{19}\]  
where:  
1. \(\text{d}^{\text{DL}}(\frac{W\cdot\text{s}}{\text{Kg}})\) is the SAR (whole-body or localized) value for DL multiplied by the time spent in the configuration: \[d^{DL}=T_{d}^{DL}\cdot SAR^{DL}\] (20) With \(\text{T}_{\text{d}}^{\text{DL}}\) the downlink exposure duration in seconds (s) and \(SAR^{DL}\) the whole-body or localized \(SAR\) induced by the BS in \((\frac{W}{\text{Kg}})\). In this study, we focus on the whole-body \(SAR\): \[SAR^{DL}=\sum_{BS_{i}}S_{BSi}\cdot SAR_{ref}^{DL}\] (21)  
\(SAR_{ref}^{DL}\) is the downlink reference whole-body or localized \(SAR\) in \((\frac{W}{\text{Kg}}\) per \(\frac{W}{\text{m}^{2}})\), obtained by means of simulations performed for a horizontal single incident plane wave with an incident power density of 1 \(\frac{W}{\text{m}^{2}}\), using numerical electromagnetic simulation software (Simalife) and \(S_{BSi}\) the received power density due to the base station at a location \(i\) in \((\frac{W}{\text{m}^{2}})\), obtained as follows:  
\[S_{BS_{i}}=\frac{E_{BS_{i}}^{2}}{120\cdot\pi}\] (22) With \(E_{BS_{i}}\) the electric field strength in [V/m] due to a base station at a location \(i\) obtained as in Equation (10);
2. \(d^{UL}\) is \(SAR^{UL}\) (whole-body or localized) values for UL, multiplied by the actual transmit time: \[d^{UL}=T_{d}^{UL}\cdot SAR^{UL}\] (23)  
\(T_{d}^{UL}\) is here assumed to be 35 \(s\) within the considered time frame of an hour [16] and \(SAR^{UL}\) the uplink whole-body or localized \(SAR\) induced by the BS obtained as in Equation (14).
## 7 Using IETF Standards to Realize the Internet of Things
### Bringing Semantics to the Web of Things  
Semantics define a globally interpretable significance to data. Adding semantics to the IoT allows data that originates from different sources to be unambiguously accessible and processable across different domains and by different users. This allows describing data that is collected from the real world which helps automated processing and integration of said data into applications. Semantic descriptions are particularly useful in M2M environments where a high level of autonomy is assumed. Said descriptions can also help to facilitate discovery and management of IoT devices and their resources. In [146], the authors mention that the dynamicity and pervasiveness of the IoT domain poses additional challenges for semantic description when compared to conventional web service environments. More specifically, the volatility of the sensor data and the large scale of the IoT are new challenges when defining semantics when compared to traditional web services.  
When looking at the resources on the devices themselves (e.g., a temperature) several resource representations can be explored: ranging from plain text formats, through formats defined by the IPSO alliance [147] to complex semantic representations using ontologies that are adapted to the specific applications and domains as described for instance in [148]. Furthermore, the SPITFIRE project [149] has defined vocabularies to describe sensors and to integrate them with W3C's Linked Open Data cloud [150]. This allows linking sensor data with other data that is already available on the World Wide Web (WWW). This brings the potential of semantic web technology (e.g., searching and reasoning) to constrained devices, realizing a Semantic Web of Things.  
Similar to search engines in the WWW, sensors and their resources could be indexed just like regular web pages and made available to Internet users and other IoT devices. Of course, issues such as time dependent aspects should be taken into consideration (e.g., indexing a temperature sensor) introducing novel challenges and opportunities. Platforms like Cosm [151] already allow to make sensor data publicly available, browsable and searchable but lack the ability to actively crawl and index sensors.
# Neural-Network-Based Synchronization Acquisition with Hankelization Preprocessing
## 3 Proposed Method  
### Brief Introduction of Preprocessor Design Based on Hankelization  
This paper introduces a preprocessing approach that constructs a Hankel matrix by applying the inverse Fourier transform to the correlation function, rather than directly using the correlation function as the input for the NN. SVD is applied to the Hankel matrix to extract a vector of singular values, which is used as the input for the NN. This method effectively preserves the primary signal features while reducing noise. By converting the correlation function into a structured matrix representation, the key characteristics of the signal are highlighted and efficiently utilized for further processing. This preprocessing step enhances the overall performance of the synchronization detection framework.  
Figure 1 presents the framework of the NN-based synchronization signal detection system incorporating Hankelization preprocessing proposed in this paper. The inverse Fourier transform of the correlation function maps its frequency-domain representation to the time domain, with dominant features corresponding to the peaks of the correlation function, while residual components reflect less significant contributions.
## 5 Results and Discussion
### Image Enhancement Results
#### 5.1.3 Principal Component Analysis (PCA) and Independent Component Analysis (ICA)  
PCs contain spectral information relative to minerals and whether the digital numbers (DNs) of pixels containing the target minerals had high (bright) or low (dark) values [61]. In this article, PC1 tends to represent topographic and brightness information, while PC4 may represent the characteristic information of a lithology with low DNs. Although lower-order principal components only contain a small amount of spectral information, they have a lower signal-to-noise ratio. Therefore, selecting lower-order and higher-order principal components for combination is very meaningful for emphasizing some target spectral features [72; 87; 98; 102; 103; 104]. We selected PC6, PCA5, PCA4, and PCA2 components for RGB false color composites, which can better distinguish mantle peridotite rocks from other lithological units. The RGB combinations of PCA5-PCA4-PCA2, PCA2-PCA4-PCA5, and PCA6-PCA4-PCA2 demonstrated the distribution of mantle peridotite in blue or purple where it was prominently emphasized (Figure 8a-c). Meanwhile, the main IC component obtained by independent component transformation and the main PC component band combination were used to maximize the expression of component information. Results revealed that combining ICA1-ICA7-ICA6 in the RGB channels enhanced not only the differentiation of mantle peridotite in a red color but also the identification of southeast slate in a vi  
Figure 7: Lithological discrimination using OLI band-ratio color composites of b5/b4-PCA4-b4/b7 in RGB.
###### Abstract  
Water-sand inrush disasters are frequently encountered during underground engineering construction in karst terrain. The objective of this paper is to study the failure mechanism and evolution characteristics of water-sand inrush caused by the instability of filling medium in karst cavity, as well as the impacts of soil compactness, hydraulic pressure and confining pressure on the instability process. In response to this purpose, a stress-controlled seepage test apparatus in consideration of particle loss was designed, and a series of seepage tests were performed correspondingly. The test results indicate that: (1) Based on the nonlinear feature analysis of water-outflow pattern, the water-sand inrush process can be divided into the \"slow flow\" stage, \"transition flow\" stage and \"pipe flow\" stage by Transition Point I, II. (2) The decreasing soil compactness and increasing hydraulic pressure both exponentially facilitate the seepage-erosion process by increasing the particle-erosion ability; the increasing confining pressure extends the \"slow flow\" stage and shortens the duration of the \"transition flow\" stage, ultimately advancing the occurrence of the \"pipe flow\" stage; the existence of critical hydraulic pressure for the seepage-erosion progress is confirmed, the occurrence of the \"pipe flow\" stage is significantly advanced once the hydraulic pressure over the critical value. (3) The particle loss caused by the seepage-erosion process is the internal mechanism of water-sand inrush, the variation characteristics of water-outflow pattern are crucial external manifestations correspondingly. Therefore, with the monitoring of water-outflow pattern variation tendency as indicators, the critical status of water-sand inrush can be near-real-time identified, which offers experimental foundation for the early warning and forecast of the occurrence of water-sand inrush.  
underground construction; water-sand inrush; failure mechanism; evolution characteristics; experimental study; seepage-erosion; warning and forecast 2020
## 4 Experiments  
### Datasets  
To verify the effectiveness of our methods, we conduct experiments mainly on three public action recognition benchmark datasets, namely UCF-11 (YouTube Action), HMDB-51 and UCF-101, which are currently challenging annotated action recognition datasets.  
**UCF-11** dataset, also known as the YouTube Action dataset, consists of 1600 videos and 11 actions: basketball shooting, biking/cycling, diving, golf swinging, horse back riding, soccer juggling, swinging, tennis swinging, trampoline jumping, volleyball spiking, and walking with a dog. The clips have a frame rate of 29.97 fps and each video has only one action associated with it. We use 1120 videos for training and 480 videos for testing (70% for training and 30% for testing).  
**HMDB51** dataset is a large collection of realistic videos from various sources, including movies and web videos. It is composed of 6766 video clips from 51 action categories, with each category containing at least 100 clips. These clips are labeled with 51 classes of human actions such as Clap, Drink, Hug, Jump, Somersault, Throw and many others. This dataset is more challenging than others because it contains scenes with more complex backgrounds. It has similar scenes in different categories, and it has a small number of training videos. HMDB-51 have three split settings to separate the dataset into training and testing videos. Our experiments follow the original evaluation scheme, but only adopt the first training/testing split. In this split, each action class has 70 clips for training and 30 clips for testing.  
**UCF101** dataset is composed of about 13,320 realistic user-uploaded video clips and 101 action categories, with each category containing at least 100 clips. The database is particularly interesting because it gives the largest diversity in terms of actions and with the presence of large variations in camera motion, object appearance and pose, object scale, viewpoint, cluttered background, illumination conditions, etc, and it is the most challenging dataset to date. There are three splits for training and testing (70% training and 30% testing). We tested our model on the first training/testing split in the experiments. Classification accuracy is used as evaluation measure.
## 2 The Principle of Uniform Accuracy Lifetime Design  
### The Principle  
Suppose the number of components in a measurement system is \(n\), \(\delta_{s}(t)\) is the accuracy loss function of the whole system at time \(t\) and \(\delta_{i}(t)\) is that of the \(i\)-th component. If their accuracy loss allowable values are known as \(\delta_{\Delta s}\), \(\delta_{\Delta 1}\), \(\delta_{\Delta 2}\),..., \(\delta_{\Delta n}\), the accuracy lifetime of the system \(T_{s}\) and that of each component \(T_{i}\) can be calculated using the following equations:  
\[\delta_{s}(t)|_{t=T_{s}}=\delta_{\Delta s}\,,\ \delta_{i}(t)|_{t=T_{i}}=\delta_{ \Delta i}\ (i=1,2,\cdots,n). \tag{1}\]  
Hence,  
\[T_{s}={G_{s}}^{-1}[\delta_{s}(t)]\,\Big{|}_{\delta(T_{s})=\delta_{\Delta s}}\,, \ T_{i}={G_{i}}^{-1}[\delta_{i}(t)]\,\Big{|}_{\delta_{i}(T_{i})=\delta_{\Delta i }}\,, \tag{2}\]  
where \(G^{-1}\) represents the inverse function of the corresponding loss function. If the accuracy lifetimes of each component and the whole system are equal to each other, that is, \(T_{1}=T_{2}=\ldots=T_{n}=T_{s}\), the system is absolutely uniform in its accuracy lifetime and does not waste resources. Otherwise, if \(T_{1}\
eq T_{2}\
eq\ldots\
eq T_{n}\
eq T_{s}\), the accuracy loss functions \(\delta_{s}(t)\), \(\delta_{1}(t)\), \(\delta_{2}(t)\),..., \(\delta_{n}(t)\) should be rationally designed and modified to achieve a uniform accuracy lifetime.
## 5 Risk Assessment Considering the Coupling Effects Between Meteorological Factors and Power Flow
### Risk Assessment Indicators  
The objective of quantitative risk assessment is to establish metrics that represent system risk. Various metrics can be utilized for risk analysis, including overload, voltage, and branch indicators. Branch risk indicators reflect the overload characteristics of a branch caused by power flow redistribution following a disconnection, which may result in overloads on other branches. Thus, this paper employs load rate and coupling coefficient as key metrics for risk assessment. Since the economic performance of power companies is closely linked to user power losses, this paper adopts the expected energy not supplied (EENS) [28] calculation as an additional risk metric.  
(1) Load Rate  
Let \(\alpha_{i}\) be the ratio of the active power of branch \(i\) at time periods \(t\) and \(t-1\) to its maximum allowable active power, reflecting the load level of branch \(i\) under stable operating conditions.  
\[\alpha_{i}=\frac{P_{t}^{i}+P_{t-1}^{i}}{2P_{\max}^{i}} \tag{34}\]  
In the equation, \(P_{t}^{i}\) and \(P_{t-1}^{i}\) represent the power flows of branch \(i\) during the cascading failure at time periods \(t\) and \(t-1\), respectively, while \(P_{\max}^{i}\) denotes the maximum allowable power flow of branch \(i\).  
(2) Coupling Coefficient  
Due to the cumulative effect of successive branch disconnections on the remaining branches, \(\beta_{i}\) is defined as the ratio of the active power variation in branch \(i\) during stages \(t-1\), \(t\), and \(t+1\) to the original active power of branch \(i\). Physically, the greater the power flow fluctuation caused by the disconnection of the previous branch, the larger the coupling coefficient becomes.  
\[\beta_{i}=\left\{\left|\frac{P_{t}^{i}-P_{t-1}^{i}}{P_{t-1}^{i}}\right|+\left| \frac{P_{t+1}^{i}-P_{t}^{i}}{P_{t}^{i}}\right|\right\} \tag{35}\]  
In this equation, \(P_{t-1}^{i}\), \(P_{t}^{i}\), and \(P_{t+1}^{i}\) represent the active power on branch \(i\) during stages \(t-1\), \(t\), and \(t+1\) of the cascading failure, respectively.  
(3) Expected Energy Not Supplied(EENS)  
The expected value of reduced power supply to users due to forced unit outages indicates the severity of the fault. Therefore, EENS is used as an indicator to assess and quantify the impact of typhoons within specific fault chains.  
\[R_{\rm E}=\sum_{s\in F}P(s)C(s)T \tag{36}\]  
In the equation, \(R_{\rm E}\) denotes the expected value of insufficient power in the system; \(P(s)\) denotes the probability of the system state; \(C(s)\) denotes the load reduction caused by fault state \(s\); \(T\) denotes the evaluation time frame (typically in hours); and \(F\) denotes the set of system states.
# Detection of Periapical Lesions on Panoramic Radiographs Using Deep Learning
## 4 Discussion
In fact, most complications in dental practice stem from misdiagnosis, which often involves missing out on noticing periapical lesions [30]. In this sense, the proposed technology could help clinicians fill dental charts, and minimize diagnosis errors in the detection of periapical lesions. In fact, a previous study has already demonstrated that AI could outperform dental specialists in detection of apical lesions [27]. This study has several limitations. It is based on panoramic radiographs from a single source restricted to patients from Brazil. Even though our model achieved excellent image recognition and detection results, data collection from multiple other sources and sites would be required in the future to increase the robustness of the algorithm and establish generalization of the applicability of the model across different sites. Another limitation of this study was the use of panoramic radiographs of only permanent dentition. Even though our study included panoramic radiographs for patients with a wide range of ages (between 11 and 84 years of age), mixed dentition was excluded, thus future studies would be needed to address an optimized algorithm for both permanent and mixed dentition. Another limitation of our study was that the OPGs used for training were only diagnosed visually by inspection of the radiograph; more accurate diagnosis necessitates the inclusion of clinical data such as percussion, thermal and electric pulp tests, which have not been taken into account here. Thus, future studies could further improve the performance of the algorithms by training them on images with diagnoses confirmed by a wider range of diagnostic techniques.
# Security in Wireless Sensor Networks: A Cryptography Performance Analysis at MAC Layer
## 6 Conclusions  
Mostly, WSNs' drawbacks are related to the limited resources (energy, computing, and bandwidth) of devices involved inside the network. Thus, WSNs could be easily taken as a target by malicious users and, due to the limited node resources, it is really hard to provide an adequate security mechanism. In this work, the security issues of two MAC protocols, BMAC and LMAC, are analyzed, tested, and compared. We have applied to these protocols different cryptographic techniques: AES, RSA, and ECC, in order to mitigate two specific attacks which could be carried out against these kind of networks leveraging the MAC protocol: the energy drain attack and the impersonation attack. We have evaluated the system under attack, and then we have applied mitigation techniques evaluating two output parameters: energy consumption and total number of received packets. Results show that security of exchanged data is guaranteed ensuring also confidentiality, integrity, and authentication, and that the LMAC protocol guarantees the best performance in comparison to the BMAC.  
Conceptualization, F.D.R. and M.T.; methodology, F.D.R.; software, M.G.S.; validation, A.F.G. and M.G.S.; data curation, M.T.; writing--original draft preparation, M.T., M.G.S. and A.F.G.; writing--review and editing, M.G.S. and M.T.; supervision, F.D.R. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflicts of interest.  
Figure 17: BMAC and LMAC comparison for ECC: Average energy consumed, total number of received and loss packets.
###### Abstract  
Quantitative assessment of the UV effects on aquatic ecosystems requires an estimate of the in-water hyperspectral radiation field. Solar UV radiation in ocean waters is estimated on a global scale by combining extraterrestrial solar irradiance from the Total and Spectral Solar Irradiance Sensor (TSIS-1), satellite estimates of cloud/surface reflectivity, ozone from the Ozone Monitoring Instrument (OMI) and in-water chlorophyll concentration from the Moderate Resolution Imaging Spectroradiometer (MODIS) with radiative transfer computations in the ocean-atmosphere system. A comparison of the estimates of collocated OMI-derived surface irradiance with Marine Optical Buoy (MOBY) measurements shows a good agreement within 5% for different seasons. To estimate scalar irradiance at the ocean surface and in water, we propose scaling the planar irradiance, calculated from satellite observation, on the basis of Hydrolight computations. Hydrolight calculations show that the diffuse attenuation coefficients of scalar and planar irradiance with depth are quite close to each other. That is why the differences between the planar penetration and scalar penetration depths are small and do not exceed a couple of meters. A dominant factor defining the UV penetration depths is chlorophyll concentration. There are other constituents in water that absorb in addition to chlorophyll; the absorption from these constituents can be related to that of chlorophyll in Case I waters using an inherent optical properties (IOP) model. Other input parameters are less significant. The DNA damage penetration depths vary from a few meters in areas of productive waters to about 30-35 m in the clearest waters. A machine learning approach (an artificial neural network, NN) was developed based on the full physical algorithm for computational efficiency. The NN shows a very good performance in predicting the penetration depths (within 2%).  
UV satellite measurements; radiative transfer computations; underwater irradiance 2022142278[[https://doi.org/10.3390/rs14092278](https://doi.org/10.3390/rs14092278)]([https://doi.org/10.3390/rs14092278](https://doi.org/10.3390/rs14092278))
## 3 Methodology
### Data Preprocessing  
The datasets used to evaluate the proposed system were the Asia Pacific Teleophthalmology Society (APTOS) 2019 dataset [44] and the Indian Diabetic Retinopathy Image Dataset (IDRiD) 2018 [45]. The APTOS dataset consists of 3662 retinal images of five classes (no DR, mild, moderate, severe, and proliferate) while the IDRiD dataset only consists of 516 DR images. For the imbalanced datasets, the majority of their images are in the no DR class. Table 2 presents the details of the dataset distributions. A sample of the APTOS dataset is presented in Figure 2. Likewise, the IDRiD dataset consists of the same types of images. Image enhancement techniques can vastly influence the classifier's performance and increase visibility [46; 47]. Therefore, the proposed system incorporates a series of image-processing algorithms, which can be mainly divided into four stages. A brief illustration of these stages is presented in Figure 3. The algorithms are described below.