esa-sceva/satcom-chunk-collection huggingface dataset:
# Causes of Asphalt Pavement Blistering: A Review
## 3 What Are the Potential Causes of Blistering?
### Inside Overpressure  
The currently most accredited hypothesis explains blistering with the following theory: during scorching summer days, exposure to solar radiation leads to high surface temperature values [55]. High air temperatures contribute to such conditions because the cooling capacity of the air is reduced. This heating process also affects any gas and/or water within the wearing layer or at the interface with the lower layers [56]. Changes in the water-heat regime in the base and lower layers of the pavement and the subgrade cause blistering as a deep process [57]. Soil moisture retention properties for unbound materials modify the water content during the seasonal drying and wetting cycles [58]. Increases in moisture inside the granular layers due to rainy and humid periods change the volume and strength of the granular soils [59] and contributes to blister development inside the upper dense layers [49] (Figure 3).  
Volumetric expansion and evaporation can be countered by the wearing course avoiding interaction with the atmosphere and by the tack coat preventing downward expansion. Thus, gas and water vapor trapped under the surface break the interface bonding between layers [60]. The pressure causes bulges when it overcomes the opposing forces [57] (Figure 4).  
A surface bulge depends on a pressure increase that lifts the wearing layer to a stable condition. According to these hypotheses, exposure to solar radiation and inside overpressure are the driving variables behind blistering [57]. When solar radiation reaches the pavement surface, some is absorbed, some is reflected, and the remaining is transmitted [61]. The asphalt reflection coefficient depends on several factors (e.g., the age of the material, type of aggregates, and surface color). Since traffic erosion leads to lightening of the pavement color and increases the albedo (e.g., from the initial values of 0.04-0.06 to 0.09-0.18), the increased reflective capacity slows down the heating and decreases the pavement peak temperature on a summer day [62]. Therefore, higher temperatures reached by a recently laid layer lead to a higher probability that secondary blistering will occur in the first hot season of a pavement laid in cold months.
## 1 Introduction
### Pupillometry  
Pupillometry refers to the measurement of pupil size which has been used in experimental psychology to evaluate memory processes, task performance dynamics, fluctuations in autonomic arousal and alertness, and attention studies [12; 17]. [PERSON] et al. [12] reported task-evoked pupillary response to be a reliable, albeit indirect measure of cognitive processing load and reported it to be reflective of task demands and stimulus features in language processing tasks. Similarly, evidence exists that pupil size is sensitive to a variety of auditory stimuli, including elements such as syntactic complexity, speech intelligibility, type of background noise, and demands for divided attention [17]. In studies measuring listening effort, pupil size is recorded simultaneously with the presentation of auditory stimuli, such as speech, typically using infrared eye tracking technology [12].  
With the potential obstacles in communication success and the increased load of processing disordered speech or perceptually evaluating abnormal voice qualities, pupillometry may serve as an additional tool for assessing the amount of effort normal hearing/speaking listeners expend during communication with dysphonic individuals [12; 13; 17; 18]. Accordingly, it is of interest to measure listeners' pupil dilation while listening to and perceptually rating dysphonic voices, so the link between objective and subjective measures can be examined. Therefore, this study was designed to evaluate the perception of the vocal feature of strain, as well as the perceived and objective listening effort (through pupil responses) associated with AdSD speech samples. More specifically, the following research questions were addressed in this study:  
1. Do normal hearing adult listeners expend effort while listening to intelligible speech samples from talkers with different degrees of AdSD severity?
2. Is there a relationship between the auditory-perceptual ratings of vocal strain and listening effort for these AdSD talker samples?
3. What is the relationship between the pupillometric measures of listening effort and perceived vocal strain and listening effort ratings, when listeners are presented with AdSD speech samples?
# Linewidth Measurement of a Narrow-Linewidth Laser: Principles, Methods, and Systems
## 3 Research Progress  
Guided by the basic principles of the aforementioned linewidth characterization methods, researchers have constructed different types of testing systems and conducted measurements and calculations on laser linewidths of different magnitudes. A systematic study was conducted on the system noise and beat frequency power spectrum fitting that constrain linewidth characterization.
## 3 Deep Learning Techniques for Cardiovascular Disease Prediction
### Role of Vanilla Recurrent Neural Network for Cardiovascular Disease Prediction  
Vanilla Recurrent Neural Network (VRNN) is simplified variant of RNN that consist of three units: an input unit, hidden unit, and output unit, along with a context unit in its hidden layer. The VRNN is a bit different from traditional NN because it includes a feedback loop that increases the model's learning capability.  
In a VRNN, there is a loop through which information flows. Before making any decision, the VRNN considers both the previous and the current input to predict the next word. During backpropagation, the weights of the neurons are updated. Several elements need to considered such as activation function, transfer function, bias, learning rate, and error. The sigmoid activation function is employed in the output layer, which outputs a number between 0 and 1.  
To process time series data, VRNN uses three layers, i.e., input, hidden and output. The functionality of each layer is described below.  
Input Layer: at each time step \(t\), VRNN takes the time series data \(x_{i}\) as an input. The input layer is not responsible for training. It simply passes the input to the hidden layer.  
Hidden Layer: the primary function of this layer is to store the past information and to capture dependencies. In Equation (3), \(h_{t}\) is the hidden state with respect to time, \(x_{i}\) is the input at time step \(t\), \(W_{h_{t}}\) is the input connection weight matrix, \(b_{h}\) is the bias for hidden state, and \(\sigma\) represents sigmoid activation function. To capture dependencies, the hidden state stores the information of both the current state \(x_{i}\) and the previous state \(h_{t-1}\):  
\[h_{t}=\sigma(W_{hr}.h_{t-1}+W_{hx}.x_{i}+b_{h}) \tag{3}\]  
Output Layer: the main functionality of output layer is to produce the output \(y\) at each time step \(t\). Equation (4) presents the equation for output layer, where \(y\) presents the output, \(W_{yh}\) is the weight matrix, and \(b_{y}\) is the bias [38; 39]:  
\[y=W_{yh}.h+b_{y} \tag{4}\]  
\[y=1/(1+e^{-x}) \tag{5}\]  
In Equation (5), we present the sigmoid activation function formula through which we have computed our results.
###### Abstract  
Boron-Neutron Capture Therapy (BNCT) is a tumor-selective radiotherapy, based on the nuclear capture reaction \({}^{10}\)B(n,\(\alpha\))\({}^{7}\)Li producing short range \(\alpha\)-particles and recoiling \({}^{7}\)Li nuclei exclusively confined to boron-enriched cancer cells. These particles possess high Linear Energy Transfer (LET) and mainly generate clustered DNA strand breaks, which are less faithfully restored by intracellular repair. Mis-rejoined breaks yield chromosome aberrations (CAs), which, for high-LET radiation, are more complex in nature than after sparsely ionizing photons/electrons used in conventional radiotherapy, which leads to increased cell-killing ability. However, such a radiobiological tenet of BNCT has been scantily studied at the DNA level. Therefore, the aim of this work was to evaluate CAs induced by BNCT in comparison to X-rays in genomically stable normal human epithelial mammary MCF10A cells. Two Fluorescence In Situ Hybridization (FISH)-based techniques were applied to clyculin A-induced prematurely condensed chromosomes: Whole Chromosome Painting and multicolor(m)-FISH. Not only did BNCT induce a greater CA frequency than X-ray irradiation, but m-FISH karyotype-wide analysis confirmed that CAs following BNCT exhibited a much higher degree of complexity compared to X-rays. To our knowledge, this is the first time that such evidence supporting the radiobiological superiority of BNCT has been shown.  
BNCT; BPA; FISH techniques; chromosome aberration; complex DNA damage; high-LET radiation
## 3 Research in Right-of-Way
### Situational and Height Measurements  
As part of the research program, the situational and height measurement of the measurement points was performed. Field measurements refer to the observation line consisting of 37 stabilized points along the right-of-way (Figures 11 and 12). The distance between the points was 50 m.  
Classic geometric leveling was performed with the use of an optical leveler with an accuracy of +/ - 1-2 mm. At the same time, a measurement was performed with the use of the GNSS (global navigation satellite system) technique with the use of the RTK (real-time kinematic) method. The real state of the road surface was recreated using low-ceiling photogrammetry with the use of an unmanned aerial vehicle (drone). Detailed data of the raid are presented in the next chapter of this article.  
The results of the obtained ordinates of the observation line measurement points with the division into the measurement methods performed are shown below (Figure 13).  
Figure 11: Location of the measurement line with the marked area revealing linear discontinuous deformations.  
Figure 12: Stabilization of points along the right-of-way.  
As can be seen from the graph above, a good agreement of the measurements performed with the use of three different measurement methods was obtained. Due to the length of the measurement section and the distribution of measurement points every 50 m, using the classic methods, the revealed deformations of the pavement were not sufficiently captured. Hence, in the further part of the analysis, the possibilities of using data from low-ceiling photogrammetry for detailed identification of the resulting zones of linear deformation were examined.
## 3 Proposed Method
### FL for Intrusion Detection  
As previously stated, in our FL Model, all K clients' train a local model with the same shared global model, but it is trained on various local datasets rather than on a central server. Following that, they communicate the learning from these local trainings to an aggregation server via an SSL/TSL authenticated connection via the communication administrator (e.g., gRPC channel [26]). The aggregation server integrates all of them and produces an updated global model with optimal parameters. The letter \(w\) stands for the starting weights, and R represents the number of FL rounds, which will be repeated before reaching a convergence level. When each local client's weight is submitted to the aggregation server during communication round \(t\), the following equation (Equation (1)) adapted from the FedAvg algorithm [27] can be used to update the model weights.  
\[W_{t+1}=\sum_{k=1}^{K}\frac{n_{k}}{n}w_{t+1}^{k} \tag{1}\]  
where \(n\) is the total size of all client datasets, and \(n_{k}\) represents the size of each client dataset. \(W_{t+1}\) is the updated global model after the iteration.  
Figure 3 depicts a scenario illustrating the interconnections between the various FL IIoT intrusion detection system participants. The server initially selects clients who have connectivity with active IIoT devices that are powered on, charging, and connected to an unmetered Wi-Fi network to take part in the FL process. After that, the different parts of the system interact in the following ways to finish the whole process:  
1. At t = 0, the server generates a neural network model from a global intrusion detection model. At this point, the number of hidden layers, neurons, epochs, etc. is counted. The symbol w denotes the model's initial weight.
2. Every k client (k \(\in\) [1,..., K]) need to utilize the global model download it, regardless of whether they contribute to the FL process or not. With their own private data, each of the K clients retrains the global model locally in parallel and creates a fresh set of local weights \(w_{t+1}^{k}\).
3. The designated clients use the data collected from the IIoT devices under their control to improve the model under investigation while maintaining the privacy of their local data.  
4. To protect the privacy of the clients, only the updated model parameters for the improved intrusion detection model are sent to the central server.
5. Once all the changes are received, the server combines the weights from the various node models to produce a new improved model (Equation (1)). The FedAvg method is used for the aggregation. In this method, the parameters are evaluated based on the size of the dataset at each node.
6. The updated model parameters are pushed back to the clients by the central server.
7. Each client uses the new model parameters and makes changes to them based on the new data.
8. For continuing model learning and improvement, steps 4, 5, 6, and 7 are repeated.
# Assessing the Added Value of Sentinel-1 PolSAR Data for Crop Classification
## 2 Materials
### Satellite Data  
#### 2.3.1 Sentinel-1 Data  
The Sentinel-1 satellites are equipped with a dual-polarized (VV, VH) C-band SAR sensor, operating at 5.045 GHz [97]. The sensor's incidence angle ranges between 29.1\({}^{\circ}\) and 46.0\({}^{\circ}\)[11] and data can be collected in both ascending and descending orbit pass directions. The Sentinel-1 mission operates in four different acquisition modes (Stripmap, Interferometric Wide swath (IW), Extra-Wide swath, Wave) [12] and has a high revisit time of 6-12 days (now 12 days due to the loss of Sentinel-1B). In this work, we used 24 Sentinel-1A and Sentinel-1B acquisitions from mid-January to late October of the 2018 growing season. The images were downloaded as Level-1 SLC products and were acquired in IW swath mode at ascending orbit direction. Each Sentinel-1 IW SLC product captures approximately a swath of 250 km in length, at 5 m \(\times\) 20 m spatial resolution in the azimuth and range direction, respectively. Additionally, it comprises 3 sub-swaths per polarization and each sub-swath is divided into 9 bursts.  
Figure 1: Study area located in Navarre, Spain. The scale is referred to the map on the left where the fields of interest are illustrated in light blue color. On the upper right map, the dark blue line indicates the borders of the entire province of Navarra. In the lower right map, we highlight the location of Navarra with respect to the entire country.  
Figure 2: A timeline of the growth stages of the major crops in the study area, reworked from [96], presented with Sentinel-1 (red color) and Sentinel-2 (blue color) acquisition dates.
# An Agent-Specific Stochastic Model of Generalized Reaching Task Difficulty
## 3 Application of the Stochastic Model to an Example Motor Task  
The general model proposed is valid for any generalized reaching motor task observed at a generic end-effector (e.g., foot, hand) of a moving agent (e.g., human, robot). In this study, the stochastic difficulty model is demonstrated for the gait of healthy human subjects of different age groups, where the rhythmic stride movement is considered as an example of a generalized reaching motor task. In this example, the ankle of the swing foot is considered as the agent's end-effector that during each stride of the gait cycle follows a repetitive spatial trajectory, traveling a distance equal to the stride length along the direction of forward progression [32]. The experimental dataset, publicly available in [33], consists of the subjects' kinematics captured during multiple overground walking trials at three speed levels: slow, comfortable, and fast. The dataset also provides information about sex and age of subjects and their anthropometric features (mass, height, and legs' length).
## 3 Results and Discussion
### Analysis of Experimental Results
#### 3.4.4 Comparative Experiment for Different Algorithms  
In order to compare the improved YOLOv4 network with the mainstream object detection network used at present, faster R-CNN with relatively high accuracy in the two-stage target detection network and YOLOv3, YOLOv3-tiny, YOLOv4, and YOLOv4-tiny in the single-stage target detection network were selected to conduct comparative tests on dataset 1. All algorithms were trained with pre-trained weights. The experimental results are presented in Table 5. Among the several algorithms, the improved YOLOv4 algorithm achieved the best detection accuracy. Compared with the original YOLOv4 network, the improved YOLOv4 detection speed increased by 65.85% to 68 FPS. Although YOLOv3-tiny and YOLOv4-tiny were better than the improved YOLOv4 in terms of detection speed, the detection accuracy was lower. The improved YOLOv4 algorithm was better balanced and could guarantee the high detection accuracy and speed of the model simultaneously.
## 3 Preliminaries  
The multi-BSM is used to detect anomalies in the communication of the connected vehicles, i.e., position falsification attacks, to be more specific. The anomaly detection algorithm runs on the RSUs which are better equipped with more computational resources. It is assumed that all RSUs are connected with each other and the RSUs have real-time communication with a centralized database that contains information about the flagged vehicles and all other relevant information. Multi-BSM is better suited for an urban environment as it is more developed compared to rural areas.
## 4 Conclusions  
In the present work, we defined a suitable procedure for the ammonium removal from landfill leachate by means of multiple recycling of struvite residues, obtained through an acid decomposition phase. The experimental results showed that struvite dissolution is not feasible by using sulfuric acid or nitric acid. In fact, with H2 SO4 and HNO3, a maximum \(n\)(N):\(n\)(P) ratio of only 2 and 6.5 mmol (N):mmol (P) were reached, respectively. On the contrary, high ammonia releases of up to 1.5 mmol (N)/g and NH4+/PO43- molar ratios of around 38 mmol (N)/mmol (P) were obtained with HCl and CH3 COOH at 50 \({}^{\circ}\)C and pH 5.5. The characterization of residues proved that MgHPO4\(\cdot\)3H2O was the main product of acid decomposition treatment. The liquid phase, due to its high ammonium concentrations, could be exploited as a fertilizer source. The multiple recycling tests showed a progressive reduction of ammonium removal by exclusively exploiting struvite residues as a magnesium and phosphorus source for the treatment of raw leachate. In these tests, after the fifth cycle, the efficiencies were of about 54% and 23% when performing the pH correction with NaOH and air insufflation, respectively. On the other hand, supplementation of P and Mg reagents at a very small molar ratios (\(n\)(N):\(n\)(Mg):\(n\)(P) = 1:0.05:0.05) resulted in a stable NH4+ removal that remained around 82%; this is a consequence of the struvite regeneration during each treatment cycle. The developed method is economically suitable; in fact, with respect to the expense required for the conventional struvite process, a cost saving of about 74% is achievable. Furthermore, the process is even more advantageous because it is easily managed and can be applied with simple equipment. Indeed, each phase is subsequently conducted in the reactor without the need for complex purification treatments or drying phases of recovered solids. Currently, the process is being tested in the industrial treatment plant of ECONET Company, situated at Lamezia Terme in Southern Italy.  
This work was funded by ECONET srl.  
[PERSON] planned the work, analyzed the results and prepared the manuscript. [PERSON] and [PERSON] performed the experiments. [PERSON] contributed to the analysis of the data and revised the manuscript.  
The authors declare no conflict of interest.
## 3 Results
### Logistic Regressions
#### 3.2.2 Results for Epoch 1 (0-3 Years)  
No significant results were observed with reference to the predictive role of the PVF, GSM, EpF, and REp on SEPE memories considering epoch 1. It is probable that the low number of data collected for epoch 1 (n = 51) and the low number of memories that were structured as SEPE (n = 17) (see descriptive statistics) did not allow for a sufficient power in the statistical analysis to highlight any significant relationships.  
However, for a descriptive purpose, the co-presence of SEPE and the other types of memories was analyzed: SEPE was present in 22.2% of cases with PVF, in 6.7% with EpF, in 7.7% with Rep, and in 0% of cases containing GSM.  
The relationship between the number of Non-Single Episodic Events (NSEpE) and the presence of SEPE memory was evaluated using Logistic Regression analysis. Results showed that as the number of NSEpE increases, the likelihood of SEPE significantly decreases (B = \(-\)4.10 (SE = 1.14), Wald's Chi\({}^{2}\) = 12.93 (df = 1); \(p\) < 0.001, Exp(B) = 0.017).