esa-sceva/satcom-chunk-collection huggingface dataset:
# Focal Dice Loss-Based V-Net for Liver Segments Classification
## 2 Materials and Methods
### Convolutional Neural Network
#### 2.2.1 Focal Loss  
In order to ease the convergence process and to perform the desired optimization of the task, the loss function has to be selected accurately. [PERSON] et al. introduced the concept of focal loss [55]. The main idea is that, for detectors (but we can extend this consideration to semantic segmentation classifiers), a mechanism is needed to focus on hard samples (i.e., samples difficult to classify), avoiding that the gradient is overwhelmed by the larger number of falsenegatives. According to the convention introduced in [55], we can define the Cross-Entropy (CE) loss as:  
\[CE(p,y)=\begin{cases}-\log(p)&if\ y=1\\ -log(1-p)&if\ y=0\end{cases} \tag{1}\]  
where \(y\in\{0,1\}\) is the binary ground-truth label and \(p\in[0,1]\) is the probability estimated from the model when \(y=1\). [PERSON] et al. introduced \(p_{t}\) for notational convenience:  
\[p_{t}=\begin{cases}p&if\ y=1\\ 1-p&if\ y=0\end{cases} \tag{2}\]  
so that we can rewrite \(CE(p,y)=CE(p_{t})=-log(p_{t})\). A weighted version of the cross-entropy can be introduced. For instance, let \(\alpha\) be a weighting factor in the range \([0,1]\), so that \(\alpha\) is adopted for class \(y=1\) and \(1-\alpha\) is exploited for class \(y=0\). Typically, \(\alpha\) may be chosen by the inverse class frequency [55]. Introducing \(\alpha_{t}\), analogously to \(p_{t}\), it is possible to define the \(\alpha\)-balanced CE loss as:  
\[CE(p_{t})=-\alpha_{t}\log(p_{t}) \tag{3}\]  
In order to redefine the cross-entropy loss, [PERSON] et al. added a modulating factor \((1-p_{t})^{\gamma}\) to the CE loss, where \(\gamma\geq 0\) can be found with cross-validation, and it is called the focusing parameter.  
Then, the CE Focal Loss (FL) is:  
\[FL(p_{t})=-(1-p_{t})^{\gamma}\log(p_{t}) \tag{4}\]  
The authors noted that this formulation has two main properties:  
1. The loss is unaffected when there is a misclassification with a small \(p_{t}\); meanwhile, if \(p_{t}\) is large (near 1), the loss for well-classified examples is reduced. This means that easy examples are not penalized, if compared to \"hard\" examples;
2. The rate with which easy examples are down-weighted is governed by the focusing parameter \(\gamma\). For \(\gamma=0\), the FL corresponds to the standard CE, whereas for larger values of \(\gamma\), there is an increase in the modulating effect. [PERSON] et al. found \(\gamma=2\) as the best value in their experiments [55]. Furthermore, \(FL\) can be weighted, so that the \(\alpha\)-balanced FL can be defined as: \[FL(p_{t})=-\alpha_{t}(1-p_{t})^{\gamma}\log(p_{t})\] (5)  
According to [55], this yields a slight improvement in accuracy, over the naive non-\(\alpha\)-balanced form.
## 4 Performance Evaluation
### Simulation Environment  
#### 4.1.1 Dataset  
To evaluate the proposed work, we used a dataset of traces from the University of Southern California (USC) [32]. These traces were real-time mobility measurements collected from the USC campus, which comprised timestamp information collected through VPN sessions. During recording the dataset, it was compulsory for each user to become connected to a VPN session prior to accessing wireless connection. This mechanism resulted in recording the maintenance of the user accessing the network. Campus environments were chosen for their comprehensive, high number of active users and abundance of location samples. The dataset traces were collected from six different buildings on the campus. The buildings included in the dataset consisted of a cafeteria (location 1), a computer lab (location 2), residential facilities (location 3), a sports area (location 4), a library (location 5), and a campus hospital (location 6). The dataset incorporated users' profiles, including their IDs, along with information about Wi-Fi connections, such as IP addresses, device MAC addresses, and the date and time of each connection. Mobility traces were recorded over various days and months, covering both weekdays and weekends. The dataset contained user log information collected from the access points, capturing the activity of every user accessing the campus internet. The data were recorded at one-second intervals, resulting in a dataset comprising approximately 7,776,000 samples. However, training machine learning models on such a large dataset requires significant computational resources and time. To facilitate simulation, a random selection of 100,000 samples from each month was made, yielding a dataset of 300,000 samples. This dataset was then divided into two subsets: 70% (210,000 samples) was for training and 30% (90,000 samples) was for testing. After preprocessing the data and removing noise, we selected the traces for January only. The mobility traces in the dataset were then divided based on the location. The January traces were utilized and split into two parts for training and validation purposes. We deployed the Gaussian process and RFR for classification. Figure 5 illustrates several of the dataset's samples with various features such as time, IP address, MAC address month, etc.
# Multiple-Diffraction Subtractive Double Monochromator with High Resolution and Low Stray Light
###### Abstract  
Spectrometers play a crucial role in photonic applications, but their design involves trade-offs related to miniaturization, spectral fidelity, and their measurement dynamic range. We demonstrated a high-resolution, low-stray-light spectrometer with a compact size comprising two symmetric multiple-diffraction monochromators. We analyzed the spectral resolution and stray light and built a platform with two double-diffraction monochromators. Multiple diffractions on one grating increased the spectral resolution without volumetric expansion, and the subtractive double-monochromator configuration suppressed stray light effectively. The simulation and experimental results show that compared with single diffraction, repeated diffractions improved the resolution by 5-7 times. The spectral resolution of the home-built setup was 18.8 pm at 1480 nm. The subtractive double monochromator significantly weakened the stray light. The optical signal-to-noise ratio was increased from 34.76 dB for the single monochromator to 69.17 dB for the subtractive double monochromator. This spectrometer design is promising for broadband high-resolution spectral analyses.  
double monochromator; multiple diffraction; diffraction grating; stray light; spectral resolution; optical signal-to-noise ratio Article
## 3 Experimental Results and Analysis
### Performance Comparisons of Different Modeling Methods  
To verify the superiority of the modeling method selected in this paper, we use different methods to establish the mapping model between the feature parameters vector and the MOS value. The specific results are listed in Table 2, and the bold to show the superiority of the modeling method.  
As listed in Table 2, compared with Bayesian regression, LASSO, SVR, decision tree, random forest, Xgboost method, LightGBM method and ElasticNet regression, the QoE model established by using the ridge regression method can obtain the excellent performance. This is because the data volume of the existing datasets is still small, and there is a correlation between the feature parameters such as video quality, fluency and volatility, which is more suitable for the ridge regression method. Therefore, the ridge regression method is adopted as the modeling method in this paper.
## 4 Wireless Communication Technologies for IoT-Enabled Data Transmission
### Near-Field Communication, Radio Frequency Identification, Zigbee and Bluetooth  
The technologies Near-Field Communication (NFC), Radio Frequency Identification (RFID), Zigbee and Bluetooth operate in short range data transmission with transmission distances from a few centimeters up to a few hundred meters and use unlicensed frequency bands [79; 80; 81; 83]. NFC uses the frequency band of 13.56 MHz and provides the shortest transmission range with up to 10 cm [83]. Important advantages of this technology are its low costs, international standardization and easy integration into existing processes because NFC tags are readable with almost every smartphone [82]. An interesting aspect is the possibility of using energy harvesting to power passive NFC tags, making them independent from a battery and more sustainable [82]. Zigbee and Bluetooth enable data transmission over a range of up to 100 m [81; 82]. Both are technologies with relatively low costs and low power consumption [81]. On the one hand, Zigbee can be preferred over Bluetooth when a large number of sensor devices should be connected, or a flexible network structure is needed [84]. On the other hand, Bluetooth can outperform Zigbee regarding energy efficiency, especially when the newest Bluetooth Low-Energy (BLE) standard is used [83]. Low-cost and low-power communication are also characteristics of RFID, when passive or semi-passive tags are used. These tags send their data only by reflecting or modulating the signal coming from the reader, although active tags have their own battery which enables a stronger signal and a larger range [79]. For passive and semi-passive tags, the data transmission range is limited to 10 m, and for active tags, up to 100 m is possible [85]. In food supply chains, RFID applications were originally intended to replace optical barcodes for traceability purposes [79; 86]. However, interest has grown in further applications of RFID tags because they are robust, able to work under harsh conditions and quickly installed [87]. If sensors are integrated in RFID tags, they can enable automatic identification of goods, e.g., a pallet with fruit, combined with the ambient parameter history profile of that pallet [87]. Drawbacks of RFID are readability problems when surrounded by metal or products with high water content, and limited real-time data collection during transport [85; 87]. Furthermore, the lack of standardized global frequency use hinders international RFID applications [85]. These are major disadvantages for applications in fresh fruit and vegetable supply chains, which often operate internationally. The short data transmission range of RFID, but also of NFC, Bluetooth and Zigbee is an additional limiting factor that obstructs many IoT applications [80]. Therefore, various authors investigated the combined use of short range and other wireless communication technologies to overcome the disadvantage of short transmission distance. Some examples are presented at the end of this section.
# Microscopic Numerical Analysis of Sand Liquefaction Under Subway Train Load
###### Abstract  
Long-term vibrations from metro trains can cause liquefaction of water-rich sandy soil foundations, affecting the safety of operational tunnels. However, existing liquefaction studies mainly focus on seismic loads, and the macro-meso-mechanical mechanisms of liquefaction induced by train vibration loads remain unclear, which hinders the establishment of effective liquefaction prediction and evaluation methods. To investigate the microscopic mechanisms underlying sand liquefaction caused by train-induced vibrations, this study employs PFC3D discrete element software in conjunction with laboratory experiments to analyze the microscopic parameters of the unit cell. The findings indicate that the coordination number, mechanical coordination number, porosity, contact force chains, and strain energy all decrease with increasing vibration frequency. Conversely, the pore pressure, anisotropy, and energy exhibit opposite trends, continuing until the sample reaches a state of liquefaction failure. Notably, when the dynamic stress amplitude increases or the loading frequency decreases, the rate of reduction in coordination number, mechanical coordination number, porosity, contact force chains, and strain energy becomes more pronounced. Similarly, the rate of increase in pore pressure and anisotropy is more significant under these conditions. The research findings can provide a reference for the design of metro projects and liquefaction mitigation measures, thereby enhancing the safety and reliability of urban metro transportation systems.  
sand liquefaction; numerical simulation; pore pressure; contact force chain; structural anisotropy; microscopic energy Article
## 3 Experimental Results and Analysis
### Shear-Thinning Behavior Characterization  
Shear rheology introduces another key variable, the apparent viscosity \(\eta_{\rm a}\), defined as the ratio of shear stress to shear rate. As depicted in Figure 8, with increasing shear rates in the rheometer, slurries at different volume concentrations exhibited a continuous decrease in apparent viscosity, indicating a shear-thinning behavior of the debris flow slurry. Observing the relative positions of these curves revealed that higher volume concentrations corresponded to higher positions on the graphs, showing a positive correlation between apparent viscosity and volume concentration. This suggested that altering the solid volume concentration influenced the shear-thinning process of the slurry. Further analysis of these curves revealed distinct shear-thinning behaviors at different shear rates. At lower shear rates (\(\gamma<10\) s\({}^{-1}\)), the apparent viscosity of the slurry drastically decreased with increasing shear rates, whereas at higher shear rates (\(\gamma>10\) s\({}^{-1}\)), the apparent viscosity decreased more gradually. Overall, the rate of the decrease in apparent viscosity augmented initially with an increasing shear rate and then slowed down gradually, and the decrement in apparent viscosity diminished as the shear rate increased, approaching zero. Therefore, it is evident that lower shear rates exacerbated the shear-thinning behavior of the debris flow.  
Figure 8: Curve of apparent viscosity with shear rate in a semilog coordinate.  
Figure 7: Relationship between viscosity coefficient and solid volume concentration.  
As shown in Figure 9a, in a logarithmic coordinate system, the relationship between apparent viscosity and shear rate for the debris flow complied with the expression:  
\[\text{lg}\eta_{\text{a}}=a+b\text{lg}\gamma \tag{6}\]  
where \(a\) and \(b\) are fitting parameters, with specific values listed in Table 4. Plotting the data from Table 4 in Figure 9b revealed that both \(a\) and \(b\) were linearly correlated, respectively, with the solid volume concentration of the debris flow. Notably, the correlation coefficient (R\({}^{2}\)) for \(a\) surpassed 0.997, indicating a stronger association between \(a\) and the solid volume concentration of the debris flow. In fact, \(b\) describes the variation in apparent viscosity in relation to shear rate, specifically reflecting the fluid's shear-thinning behavior, making it an indicator of the extent of this thinning behavior.  
In conclusion, Equation (6) can be expressed as a function involving both solid volume concentration and shear rate, yielding the new Equation (7) capable of predicting the shear-thinning behavior of the debris flow in this study  
\[\text{lg}\eta_{\text{a}}=-3.52216+10.31732C_{\text{v}}+(-0.39272+0.96176\ C_{ \text{v}})\text{lg}\gamma \tag{7}\]
# Study on Reinforcement Mechanism and Reinforcement Effect of Saturated Soil with a Weak Layer by DC
## 4 Numerical Analysis
### Parametric Analysis
#### 4.2.2 Influences of Depth of the Embedded Weak Layer  
In the numerical simulations, the depth of the embedded weak layer ranged from 0.4 to 3.0 m to investigate its influences on the improvement effect of DC to the saturated ground. The left half of each figure presents the ground without a weak layer, while the right half represents the ground with an embedded weak layer (see cases 5 to 8 in Table 4).  
The distributions of \(\mathrm{I_{r}}\) in grounds with an embedded weak layer at different initial depths are shown in Figure 13. The white curve represents the zone of \(\mathrm{I_{r}}\) = 7.7%. Figure 14 shows cloud lines of \(\mathrm{I_{r}}\) = 7.7% for different initial depths of soft interlayers. It can be found that the relative reinforcement degree to the embedded weak layer in the ground was around 0. It was also noted that as the depth of the embedded weak layer went down, the zone of compacted soil above the weak layer increased. However, the reinforcement effect to the soil below the weak interlayer decreased as the initial depth of the soft interlayer moved deeper. This is because that as the weak interlayer located deeper, the zone of the upper soil became thicker so that the damping energy that was transferred into the weak interlayer decreased; thus, the underlying soil was less compacted.  
Figure 12: Distributions of excess pore water pressure ratio in grounds with embedded weak layers of different thicknesses. (**a**) 0.4 m; (**b**) 1 m; (**c**) 1.6 m; (**d**) 2.0 m.  
Figure 15 shows that the zone with high excess pore pressure was mainly concentrated in the embedded weak layer and the excess pore pressure dissipated slowly in this layer, which directly affected the reinforcement effect. Specifically, by analyzing the pore pressure distribution, the excess pore pressure ratio in soil above the weak layer was the lowest and  
Figure 14: Cloud lines of I\({}_{\rm r}\) = 7.7% for different initial position of soft interlayers.  
Figure 13: Cloud charts of I\({}_{\rm r}\) for different initial position of embedded weak layer. (**a**) 0.4 m; (**b**) 1 m; (**c**) 2.0 m; (**d**) 3.0 m.  
did not change with the depth of the embedded weak layer, while that in the embedded weak layer was the highest. The excess pore pressure cannot dissipate in time in the weak layer, in which the maximum value of the excess pore pressure ratio decreased gradually as the embedded weak layer became deeper. Due to the influence of the embedded weak layer, the pore water in the soil below the weak interlayer could not be discharged in time, leading to a higher excess pore pressure ratio (or less compaction) than that in the ground without an embedded weak layer at the same depth.
## 3 Results
### Stiffness  
Stiffness modulus testing found that the addition of fibers increases the stiffness of the mix. Among the mixtures without recycling agents (Figure 10), almost all mixtures with fibers achieved a higher stiffness than the reference one at all tested temperatures. Only the mixture 0.1% J300 and the mixture with 10 mm-long fibers achieved lower stiffness at 0 \({}^{\circ}\)C. Furthermore, there was also a decrease in thermal susceptibility, calculated as the ratio between the stiffness at 0 \({}^{\circ}\)C and 27 \({}^{\circ}\)C. The mixture with NaOH-treated fibers had a higher stiffness than the equivalent mixture of 0.2% J300. An increase in stiffness was observed with increasing fiber content. Reduction in the fiber's length led to a slight stiffness increase at 27 \({}^{\circ}\)C and a decrease for the remaining two temperatures, resulting in one of the lowest thermal susceptibility values, together with mixture 0.3% J300.  
Figure 10: Stiffness results of the mixtures without recycling agents.  
Figure 9: Indirect tensile strength (left vertical axis) and ITSR according to EN 12697-12 [35] and modified AASHTO T283 protocols. Mixtures with recycling agents.  
Observing Figure 11, the application of the recycling agent brought about a logical reduction in the stiffness of the mixtures. After the addition of fibers, the value increased, which did not only apply to the mixture with a higher content of recycling agent (NaOH 9%SR). In addition to the previous observation, there was also an obligatory decrease in thermal susceptibility, except in the mixture NaOH 9%SR. With the \"SR\" recycling agent, there was no general increase in stiffness in the case of alkali-treated fibers compared to the equivalent mixture of J300 6%SR. The application of the \"RF\" recycling agent resulted in the highest value of thermal susceptibility in all the tested mixtures. After the addition of the fibers, there was a significant reduction in this parameter and an up to twofold increase in stiffness at 15 \({}^{\circ}\)C for the mixture with alkali-treated fibers. This mixture also surpassed the stiffness of the reference mixture at 27 and 40 \({}^{\circ}\)C.
## 2 ITS Architecture and Applications
### ITS Applications
#### 2.3.2 Traffic Management Applications  
Traffic management applications represent a second major class of ITS applications, whose main objective is to enhance the management and coordination of traffic flows and to provide various cooperative navigation services to the drivers. These applications rely on the collection and analysis of the exchanged ITS messages (_i.e._, between ITS entities) in order to build and maintain global traffic map databases. The traffic data are generally collected by the deployed road side units and/or from road sensors and are transmitted wirelessly to remote trusted data centers for further data analysis and processing. The collected data include contextual and location-based information related to vehicles, drivers and road events.  
Figure 4: Examples of road safety applications: (a) pedestrian crossing warning; (b) left turn driver assistance; and (c) approaching emergency vehicle warning.  
Figure 5: Origin-destination (OD) trip matrix.  
Once the collected data are processed and translated into meaningful information, these are delivered to the drivers through services providers to notify them of current and/or future congested areas, recommended itineraries, navigation instructions, speed limit notifications, _etc._ Moreover, these traffic management applications can enable authorities to perform advanced spatio-temporal traffic data analysis, such as an origin-destination (OD) trip matrix. As shown in Figure 5, the OD-matrix aims at estimating the traffic volumes between different origins and destinations (e.g., zones, streets, cities, _etc._) in order to better optimize the use and/or planning of future road infrastructures and buildings.  
As shown in Table 1, these applications rely on the periodic broadcast of safety messages and/or unicast V2X communications, whose critical latency should typically not exceed two hundred milliseconds. Other examples of traffic management applications include: regulatory speed limit notification, green light optimal speed advisory, electronic toll collection, vehicle highway management and may others [28, 29, 30].
## 2 Methodology
### Feature Extraction
#### 2.4.1 Local Features Extraction  
Large-scale areal settlements are simple two-dimensional surface shapes. The essential local information is the length and the direction of the extension. Therefore, we extract the skeleton line length and azimuth as local feature. The length of the skeleton line segment AB is \(L_{1}\); the azimuth of the skeleton line segment is \(\alpha\) as shown in Figure 5.  
Figure 4: Dual graph of skeleton lines.  
Figure 3: Extraction of areal settlement skeleton lines: (**a**) Delaunay triangulation; (**b**) The skeleton line of the areal settlement.  
The length of the line segment consisting of the azimuth angle \(\alpha\) denotes the direction of the line segment AB in the two-dimensional plane. In this study. the angle from the northern end of the line due north, turned clockwise to the target line, is noted as the azimuth of the line and takes values in the range 0.360\({}^{\circ}\).  
The shape features must be normalized as they should remain constant under translation, rotation, and reduction. The length of the skeleton line segment \(L_{1}\) is normalized by dividing it by the sum of the total lengths of the skeleton lines \(D\). The azimuth angle \(\alpha\) is normalized by dividing it by 360\({}^{\circ}\) according to the range of values of the azimuth angle \(\alpha\), as follows:  
\[\overline{L_{1}}=\frac{L_{1}}{D} \tag{1}\]  
\[\overline{\alpha}=\frac{\alpha}{360} \tag{2}\]