esa-sceva/satcom-chunk-collection huggingface dataset:
# Gold Nanoparticles and Nanorods in Nuclear Medicine: A Mini Review
## 4 Gold Nanoparticle and Nanorods: Clinical Aspects  
In view of their chemical and biological features, AuNPs and AuNRs can be labeled with several radionuclides, and have been proposed as innovative tools for diagnostic imaging, for the targeted delivery of drugs, and for the radio treatment of several cancer types [31; 132]. The different diagnostic and therapeutic applications of a single molecule/substance are now called theragnostic applications [133], which is one of the most promising fields of nuclear medicine [31]. Radiolabeled gold based nanomaterials have been designed to be able to link the target of interest with the highest binding specificity; however, each AuNP or AuNR showed different behavior based on its individual physical and biological properties, remaining on the cell surface or accumulating inside the cells [81].  
Several radionuclides have been proposed to label AuNPs: \({}^{198}\)Au, \({}^{125}\)I, \({}^{111}\)In, \({}^{64}\)Cu, \({}^{68}\)Ga, and \({}^{99m}\)Tc [31]. Depending on the physical properties of the radionuclide, the diagnostic imaging has different characteristics: \(\gamma\)-emitting radionuclides provide single-photon emission computed tomography (SPECT) imaging [87; 91; 134; 135], while \(\beta\)+emitting radionuclides provide positron emission tomography (PET) imaging [41; 88] or Cerenkov luminescence imaging [81]. When a \(\beta\)-emitting radionuclide is used, its application is \"radiometabolic therapy\". Most of the AuNPs were firstly studied in vitro to optimize the synthesis, and were then considered for pre-clinical evaluation to analyze the biodistribution, target uptake, and intralesional distribution. Despite a huge amount of research, no radiolabeled AuNPs have reached the human administration or final authorization for clinical use (by the Food and Drug Administration in the United States, or by the European Medicines Agency in Europe).  
Figure 8: Cell uptake studies. (**a**) Uptake of gastrin-releasing peptide receptor (GRPR)-specific AuNP [\({}^{68}\)Ga]10 into GRPR-positive PC3 cells (red) compared to GRPR-negative A431 cells (green). (**b**) Uptake of AuNP [\({}^{68}\)Ga]11 into LNCaP cells at different BSA concentrations. Adapted with permission from [130].
###### Abstract  
Background: Using Platelet-Rich Plasma (PRP) in anterior cruciate ligament reconstruction (ACLR) has been suggested to improve patient outcomes. The aim of this study was to assess the impact of PRP administration on pain, range of motion (ROM) restoration and the functional results of ACLR performed with quadriceps tendon bone (QTB) autografts. Methods: A total of 106 patients were included in this multicenter study. Fifty-two patients underwent single-bundle QTB ACLR and 54 patients underwent the same procedure with additional PRP administration. Results: Mean time of need for on-demand analgesia was 8 days in the PRP group and 11 days in no-PRP group. Symmetric full extension was restored in a mean of 40 days in the PRP group and 53 days in the no-PRP group. Ninety degrees of flexion was restored at a mean of 21 days in the PRP group and 25 days in the no-PRP group. At 18 months postoperatively, the mean side-to-side difference in anterior tibial translation with the use of an arthrometer (Rolimeter, Aircast Europa) was 1.3 mm in the PRP group vs. 2.7 mm in the no-PRP group. Mean tibial tunnel widening was 1.4 mm in the PRP group vs. 2.1 mm in the no-PRP group. The mean score in the pain section of the KOOS scale was 93 in the PRP group vs. 89 in the no-PRP group. For the IKDC scale, 53 patients in the PRP group graded A or B and 1 patient graded C. In the no-PRP group, 48 patients graded A or B and 4 patients graded C or D. Conclusions: The use of PRP in QTB ACLR may decrease the need for on-demand analgesia and accelerate ROM restoration as well as improve knee stability, lessen the extent of tibial tunnel widening and potentially diminish pain at 18 months postoperatively. Further studies will be needed to confirm all authors' conclusions.  
platelet-rich plasma; quadriceps tendon bone autograft; anterior cruciate ligament reconstruction; pain; range of motion; knee stability; tibial tunnel widening +
Footnote †: journal: Applied Sci. 2021
## 2 Literature Review
### Digital Divide Concept and Prior Research  
After the release of the reports of \"Falling Through the Net\" [33; 34], the phenomenon of the digital divide began to gain attention among governments and researchers [35]. Although, several researchers associated its denomination with [PERSON], the US Assistant Secretary for Information and Communication, however, the real origin of the \"digital divide\" denomination is still unknown [36; 37]. At first, the digital divide was understood as a gap between those who could access ICT and those who could not [38]. It was, therefore, suggested that solving the problem of the digital divide is just accommodating access to ICT. Ignoring that access is only the first step, and there is no guarantee of continued use [39; 40]. However, when the researchers began to go beyond the access differences, the original definition declined and the concept of the digital divide was extended [38]. At present, understanding this subject requires not only to integrate the disproportions relating to access to ICT but also the different ways of using it [36]. In 2005, [PERSON] [41] conducted research that illustrated that the digital divide is far from closed in the world and it was still widening in developing countries. Moreover, he contended that the digital divide is deepening in developed countries for the reason that the physical access gap had ceased widening. Another study by [PERSON] [42] concluded that in order to bridge the digital divide universally, the necessary online skills should be achieved by focusing policy not only on improving access but also investing in training. As an example, he stated that although there has been a rapid increase in the number of public schools offering Internet access during the years 2000 in USA, the support for the necessary training and staffing had lagged at the moment. Several studies on the digital divide have taken into account the digital divide at the global level. [PERSON] [43] and [PERSON] [44] investigated the digital divide across the countries of the European Union and concluded that digital inequality reflects the social-economic differences between the Member States. [PERSON] and [PERSON] [35] conducted a cross-national survey on the distribution of personal computers and the Internet, revealing that the distribution of these technologies was at a very low level in the developing countries. Apart from global digital inequalities, a country can also present a domestic digital divide, that is, differences between regions or groups of its people. During the [PERSON] and [PERSON] [35] research process, the first study investigated into personal computers and internet distribution in the demographic and socioeconomic context, based on the following factors: age, gender, ethnicity, education, income, and geography. It was concluded that inequalities in access are mainly due to differences in the levels of education and income. However, later, some researchers
## 2 Mathematical Modeling and Analysis of the Proposed Detector Algorithm
### Clutter Models
#### 2.3.2 Model 2: Targets Embedded in Rayleigh Distribution Clutter  
This model considers the same parameters as model 1, except that the shape parameter is equal to 1. The Rayleigh distribution with b and amplitude parameters normalized to \(\
u>0\) is given by the following equation [22]:  
\[p(\
u)=\frac{1}{b}e^{-(\frac{\
u}{b})} \tag{16}\]
# Investigation of Railway Network Capacity by Means of Dynamic Flows
## 4 Exemplary Application to a Sample Network
### Implementation of an Iterative Optimization Process  
One problem in running the optimization model presented in Section 3.3 only once is given in the definition of an appropriate number of trains in the set \(K_{pot}\). If, on the one hand, the number of trains that are defined to run in addition to the trains scheduled in any case is too small, all trains will be routed, but it remains unclear whether the network's capacity is already exhausted or whether additional trains could be scheduled. If, on the other hand, there are too many trains, the optimization process may not terminate within a reasonable time, since the time-expanded network grows rapidly, and with it the number of variables and constraints in the model.  
For that reason, an iterative optimization process is developed in this paper. At the beginning, only the trains scheduled in any case (\(K_{sch}\)) are defined, and the optimization model is used to check whether a valid schedule exists. In case all trains have been routed, one train is added to the (initially empty) set \(K_{pot}\) for each freight train route number. Subsequently, the model is solved again. That means that this time it is checked whether a valid schedule containing both the trains from \(K_{sch}\) and the additionally defined trains from \(K_{pot}\) can be generated. For each of the following iterations, it is evaluated for which of the freight train route numbers an additional train could be planned in the last iteration. The route numbers for which this is the case are again included in the set \(K_{pot}\) by one additional train in the following iteration. Trains that could additionally be planned in one iteration are also scheduled again in the next iterations. On the contrary, the route numbers for which no additional train could be planned in one iteration are excluded for the following iterations. As soon as there are no more trains that can be scheduled for any of the specified additional route numbers, the program terminates. The total number of trains that could be scheduled determines the capacity of the core network.  
The iterative optimization process described is depicted in the flow chart of Figure 4.  
Figure 4: Flow chart of the iterative optimization process applied.
# A Fault Detection Framework for Rotating Machinery with a Spectrogram and Convolutional Autoencoder
## 3 Proposed Framework
### OCC Algorithm with Extracted Features from 2D-CAE for Fault Detection
Conversely, latent feature vectors of the counter class (i.e., fault signals) are easily isolated with a shallow depth of the isolation tree, ultimately resulting in high anomaly scores. _Local outlier factor (LOF)_: The \(k\)-NN structure is a prominent data description technique that can accommodate the locality of training data. Owing to this advantage, the \(k\)-NN structures have been widely employed by OCC algorithms. Among various k-NN-based OCC algorithms, in this study, we focus on the LOF. The LOF algorithm takes into account the local density information in anomaly score calculations. To this end, the LOF algorithm defines the local density as the average distance across neighbors of a new latent feature vector, and it calculates the anomaly score as the ratio of the average distance between \(z_{new}\) and its neighbors to the local density of \(z_{new}\). In this algorithm, the anomaly score of \(z_{new}\), \(LOF(z_{new})\), is presented as follows:  
\[LOF(z_{new})=\frac{\sum_{i=1}^{k}\|z_{new}-z_{i}\|}{\frac{1}{(k-1)}\sum_{i,i,i \
eq j}\|z_{i}-z_{j}\|},\quad for\ i,\ j=1,\ \ldots,\ k. \tag{15}\]  
In the proposed fault detection framework, a latent feature vector with a large anomaly score is classified as a fault signal, while one having a small score can be regarded as a normal signal.
## 3 Proposed Security Protocol for Initial Attach in LTE
### Initial Attach with GUTI
#### 3.2.2 Case 2: MME Changed  
The second case is that the MME is changed, but the old MME saves the information about the UE, so it transmits the information about the UE to the new MME. UE transmits the old MME's information to encrypt GUTI, so the new MME is transmitted the encryption key from the old MME using the challenge response method. Security protocol for the second case is shown in Figure 7.
###### Abstract  
This study proposed a pedicle screw design where the proximal \(1/3\) of the screw is unthreaded to improve fixation in posterior spinal surgery. This design was also expected to reduce the incidence of mechanical failure often observed when an unsupported screw length is exposed outside the vertebra in deformed or degenerated segments. The aim of this study was to evaluate the fatigue life of the novel pedicle screw design using finite element analysis and mechanical testing in a synthetic spinal construct in accordance with American Society for Testing and Materials (ASTM) F1717. The following setups were evaluated: (i) pedicle screw fully inserted into the test block (EXP-FT-01 and EXP-PU-01; full thread (FT), proximal unthread (PU)) and (ii) pedicle screw inserted but leaving an exposed shaft length of 7.6 mm (EXP-FT-02 and EXP-PU-02). Corresponding finite element models FEM-FT-01, FEM-FT-02, FEM-PU-01, and FEM-PU-02 were also constructed and subjected to the same loading conditions as the experimental groups. The results showed that under a 220 N axial load, the EXP-PU-01 group survived the full 5 million cycles, the EXP-PU-02 group failed at 4.4 million cycles on average, and both EXP-FT-01 and EXP-FT-02 groups failed after less than 1.0 million cycles on average, while the fatigue strength of the EXP-FT-02 group was the lowest at 170 N. The EXP-FT-01 and EXP-FT-02 constructs failed through fracture of the pedicle screw, but a rod fractured in the EXP-PU-02 group. In comparison to the FEM-FT-01 model, the maximum von [PERSON] stress on the pedicle screw in the FEM-PU-01 and FEM-PU-02 models decreased by \(-43\%\) and \(-27\%\), respectively. In conclusion, this study showed that having the proximal \(1/3\) of the pedicle screw unthreaded can reduce the risk of screw fatigue failure when used in deformed or degenerated segments.  
p edic screws; partial threading; fatigue life; biomechanical analysis; spinal fixation +
Footnote †: journal: Applied Sciences  
Article
# Weigh-in-Motion Method Based on Modular Sensor System and Axle Recognition with Neural Networks
## 1 Introduction
Thus, the axle recognition task is converted into a sequence-to-sequence learning task, allowing the model to output the number of axles and the time points of axle entering and exiting platforms. This allows for determining the weighing time of both the axle and the whole vehicle to calculate the axle loads and gross vehicle weight. Transformer is a deep learning model primarily used for natural language processing and sequence generation. It captures the relationships between different parts of the input data through the self-attention mechanism, making it particularly suitable for processing long sequence data. GRU is an improved version of the recurrent neural network, designed for sequence data processing. The Transformer part effectively captures global information and long-term dependencies, while the GRU part further captures local features, making it better suited for handling long sequence data with local information. Finally, the accuracy of the axle recognition algorithm was tested on the test set and highway toll station. Additionally, the accuracy, consistency, and robustness of the modular WIM system were evaluated in the test field under different vehicle types, loads, and driving behaviors. The modular WIM system proposed in this paper has the advantages of high recognition, weighing accuracy, and traffic efficiency while meeting the requirements for accuracy, consistency, and traceability in measurement work. It is highly significant for implementing free-flow tolling and managing overload, in addition to presenting broad application prospects.
## 2 General Principle and Operation of Hydraulic Energy Regeneration and Conversion Technologies
### Prototype and Design of Hydraulic Energy Regeneration and Conversion Technologies  
Both CPR and VPR can be divided into series, parallel and series-parallel structures and energy flows, as shown in Figure 4 [53; 64; 65; 66; 67; 68]. It can be seen that the series structure means that the energy yielded by the engine is transformed into hydraulic energy by the hydraulic pump, and then it is recycled into mechanical energy used to move the vehicle through the hydraulic motor, as shown in Figure 4. Moreover, the parallel structure means that the mechanical driveline and the hydraulic driveline can drive the vehicle alone, and furthermore, the series-parallel structure allows the mechanical drive route and the hydraulic drive route to transform the form of energy by hybrid transmission and the pump/motor when necessary.  
Figure 3: General principle of hydraulic energy regeneration and conversion technologies [55; 62; 63; 64; 65]: (**a**) common pressure rail (CPR); (**b**) variable pressure rail (VPR).  
Hydraulic hybrid vehicles have their own unique characteristics because of their different structures. In Figure 4a,d, the series hydraulic hybrid vehicle has only one power element (engine or electric motor), one or two hydraulic accumulators and one independent driveline route. In parallel hydraulic hybrid vehicles, there is at least one power element and two relatively independent driveline routes, as shown in Figure 4b,e. Different from the previous two, Figure 4c,f shows that series-parallel hydraulic hybrid vehicles have at least two power elements and at least two relatively independent driveline routes. Moreover, the relatively independent driveline route has some connections that are used for energy conversion in some scenarios.  
As for the hydraulic vehicle control system, the general control process is that the top controller, i.e., the control unit, receives the speed or power information from the system input, then calculates the corresponding power distribution and generates control commands; finally, the bottom controller, i.e., the control units 1 and 2, adjusts the output flow or pressure of the hydraulic pump and hydraulic motor, as shown in Figure 5.  
Figure 4: Diagrams of energy flow in previously typical developed series (**a**,**d**), parallel (**b**,**e**), and series–parallel (**c**,**f**) hydraulic hybrid vehicles [53; 64; 65; 66; 67; 68].  
Table 2 lists the details of the characteristics, advantages and disadvantages of the three hydraulic hybrid vehicle configurations. It is not difficult to infer that the configuration of the series hydraulic hybrid is the simplest, the parallel hydraulic hybrid is moderately complex and the series-parallel hydraulic vehicle is the most complex. Obviously, the same is true for the controller.
# On the Estimation of the Moving Mass of a TMD Installed on a Lively Structure
## 3 Methodology
### Mass Modification Technique  
This technique is based on the definition of the natural frequency of an SDOF system, \(\omega_{t}^{2}=k_{t}/m_{t}\). The estimation of this natural frequency needs to be performed in at least two different scenarios: the first, in which the moving mass of the SDOF system is \(m_{t}\); and the second one, in which an additional mass \(\Delta m\) is added to it, so the new value of the moving mass is \(m_{t}+\Delta m\). The stiffness value, \(k_{t}\) remains unchanged, so the natural frequency varies from \(\omega_{t}^{2}=k_{t}/m_{t}\) in the first scenario to \(\omega_{t\Delta}^{2}=k_{t}/(m_{t}+\Delta m)\) in the second. Assuming that the added mass \(\Delta m\) is known, the ratio \(\omega_{t}^{2}/\omega_{t\Delta}^{2}\) permits an expression to directly estimate the value of the moving mass \(m_{t}\) to be obtained, as shown in Equation (7).  
\[m_{t}=\Delta m\frac{\omega_{t\Delta}^{2}}{\omega_{t}^{2}-\omega_{t\Delta}^{2}} \tag{7}\]  
To apply Equation (7), both natural frequencies need to be estimated and the goodness of these estimations may compromise the final estimation of the moving mass. To mitigate the effect of small errors during the estimation of a natural frequency, more than two scenarios may be considered. In each \(i\)th scenario, the moving mass is progressively increased, so \(m_{ii}=m_{t}+\Delta m_{i}\), leading to a set of estimated natural frequencies, \(\omega_{ti}^{2}=k_{t}/m_{ti}\). Now, the ratio \(\omega_{t}^{2}/\omega_{t\Delta}^{2}\) leads to the expression in Equation (8) which represents a straight line in the (\(\Delta m_{i}\), \(\omega_{t}^{2}/\omega_{ti}^{2}\)) plane, with a slope equal to \(1/m_{t}\).  
\[\frac{\omega_{t}^{2}}{\omega_{ti}}=1+\frac{1}{m_{t}}\Delta m_{i} \tag{8}\]  
In this way, several known mass increments \(\Delta m_{i}\) can be added to the SDOF moving mass and the corresponding modified natural frequencies \(\omega_{ti}\) can be estimated, leading to a certain squared frequency ratio \(\omega_{t}^{2}/\omega_{ti}^{2}\). Then, these data can be fitted to the linear expression in Equation (8) to get the value of the slope and, from it, the value of the original moving mass, \(m_{t}\). The different estimations of the natural frequency can be performed by applying the procedure explained in [16], consisting in fitting the experimental frequency domain data estimated by the \(H_{\
u}(\omega)\) estimator (Equation (6)) to the transmissibility function (Equation (4)). This method has the advantage of not having to apply and measure an external force. However, for the method to be reliable and less sensitive to estimation errors, three or more estimations must be performed, which may make the overall moving mass estimation process rather long.
## 3 Results  
### Neural Network Models and SHAP Analysis  
In this section, three different cases were analyzed. For each case, a neural network model was developed, and SHAP analysis was performed. SHAP analysis was conducted in order to determine the order of importance of the input features of cutting speed \(v_{c}\), feed rate \(f\), and depth of cut \(a_{p}\) based on their impact on the model outputs of \(R_{a}\), main cutting force \(F_{c}\), passive force \(F_{p}\), and feed force \(F_{f}\). Evaluation of the developed neural network models in each case was performed by calculating the following metrics for each output: coefficient of determination \(R^{2}\), mean squared error (\(MSE\)), and mean average error (\(MAE\)).
# Rainfall Estimation from Tropospheric Attenuation Affecting Satellite Links
## 3 Experimental Datasets
### Rain Attenuation Time Series  
Rain-induced attenuation \(A_{R}\) is extracted starting from the received beacon power levels that were collected by the NASA Ka-band receiver located in Milan. The typical, well-established approach to derive \(A_{R}\) from the received beacon power \(P_{R}\) is to first low-pass filter \(P_{R}\) with a typical cut-off frequency of 0.03 Hz, to remove scintillations [14]. Afterwards, rain events are identified, usually both by taking advantage of the local rain sensors (if present) and by inspecting the trend of \(P_{R}\): indeed, the impact of rain on the link might be longer than what is recorded by the disdrometer, especially at the beginning and/or the end of the event. Afterwards, rain attenuation is calculated by subtracting from \(P_{R}\) the power level that is the linear interpolation of \(P_{R}\) just before the beginning and after the end of each event [14]. Lastly, the attenuation data are averaged to obtain a sampling period equal to the one of the disdrometer (1-min per sample).  
An example of such a well-established procedure, which, for instance, is also employed in [14], is depicted in Figures 2-4, where the sequence of operations (low-pass filtering, rain event detection, and estimation of the reference power level during the rain event through linear interpolation) are applied to the received power on 7 th September 2017. Figure 5 plots the resulting rain attenuation time series.  
Figure 4: Reference power level, obtained through linear interpolation during precipitation, for the rain event on 7 th September 2017.  
Figure 3: Detection of the rain event on 7 th September 2017.  
Figure 2: Low-pass filtered signal (black line) of the Ka-band received signal (orange curve) on 7 th September 2017.