esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Experimental Programme
### Mix Design and Materials  
A total of 17 concrete mixes were produced: a NAC, eight concrete mixes with replacements of 10%, 25%, 50% and 100% of the total volume of coarse NA with coarse RA (from Valnor and Retria) and eight with replacements of 10%, 25%, 50% and 100% of the total volume of fine NA with fine RA (from Vimajas and Ambilei).  
Particles passing and particles retained by the 4-mm mesh sieve were considered, respectively, as fine and coarse aggregates. The maximum particle size used was 22.4 mm. The replacement of NA with RA was made by volume, fraction by fraction, in order to maintain, for all the concrete mixes with RA, the particle size distribution of the NA used in the NAC mix design. In this investigation, no admixtures or additions were used.  
All mixes were produced with a required slump of 125 \(\pm\) 25 mm measured in the Abrams cone test [29], in order allow a correct comparison between them. For this, a preliminary experimental phase was carried out, in which the amount of water used in the production of each concrete mix was adjusted, whenever necessary, in order to comply with this requirement.  
The composition of the NAC mix was determined by the [PERSON]'s curve method [33], in order to obtain a concrete mix included in the C30/37 strength class. The mixing proportions, in volume, of the materials used can be seen in Table 2.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Properties** & **Standard** \\ \hline \multicolumn{2}{c}{**Physical Characterization**} \\ \hline Determination of particle density and water absorption & EN 1097-6 [23] and patent by [PERSON] et al. [24] \\ Determination of loose bulk density and voids & EN 1097-3 [25] \\ Shape index & EN 933-4 [26] \\ Methods for the determination of resistance to & EN 1097-2 [27] \\ fragmentation (Los Angeles) & \\ \hline \multicolumn{2}{c}{**Chemical Characterization**} \\ \hline Determination of water-soluble chloride salts & 1744-1 Section 7 [28] \\ Determination of water-soluble sulphates & 1744-1 Section 10 [28] \\ Determination of acid soluble sulphates & 1744-1 Section 12 [28] \\ Determination of total sulphur content & 1744-1 Section 11 [28] \\ Determination of lightweight contaminators & 1744-1 Section 14.2 [28] \\ Determination of potential presence of humus & 1744-1 Section 15.1 [28] \\ Determination of water solubility & 1744-1 Section 16 [28] \\ \hline \end{tabular}
\end{table}
Table 1: Tests performed in the determination of the properties of aggregates.  
In this investigation, type I 42.5R cement was used, which has a minimum clinker content of 95% and contains up to 5% of additions.  
With respect to the RA source, four Portuguese recycling plants (Ambilei in Leiria, Vimajas in Algueirao-Mem Martins, Valnor in Avis and Retria in Porto) were considered. The recycling plants were chosen according to their geographical location, in order to collect RA from areas with different construction types and technologies. Regarding NA, crushed limestone (limestone 1 and 2--coarse aggregates) and rolled river sand (fine and coarse sand--fine aggregates) were used.
## 3 Materials and Methods
### Data Pre-Processing and Feature Selection
A group of 11 metrics is specifically dedicated to monitoring the WebDav service, providing data on HTTP request activity, thread usage for connection management, and the error or success rates of operations. Finally, 2 metrics describe network traffic, measuring the volume of data received and transmitted through the network interfaces. Upon completion of this phase, the data were organized into time windows in a clear and structured manner and divided into normal and anomalous observations, ready to be used in the subsequent training phase. The resulting dataset consists of approximately 39,000 normal observations and 200 anomalous observations, where each observation is represented by multiple consecutive time points, determined by the size of the time window. Each time point is described through the 32 features previously selected. To address the class imbalance, during the training process, Oversampling strategies were considered, such as replicating the observations belonging to the minority class, namely, the anomalies, to increase their presence in the training dataset. This approach allows for balancing the number of examples between the classes, improving the model's ability to effectively learn the distinctive characteristics of the anomalies.
## 4 Resource Allocation under Separate Power Constraints
### Subcarrier Pairing  
Substituting the power variables into the \(L\), we get  
\[L=\sum_{i=1}^{N}\sum_{j=1}^{N}\rho_{i,j}\varphi_{i,j}+\lambda_{1}P_{S}+\lambda_ {2}P_{R}+\mu_{1}Q_{1}+\mu_{2}Q_{2} \tag{23}\]  
where \(\varphi_{i,j}\) is given by \[\begin{array}{rl}\varphi_{i,j}=&\log_{2}\left[\frac{\sqrt{b_{j}(a_{i}+g_{j})}}{a_{i }\sqrt{\lambda_{2}+d_{j}\mu_{2}}+\sqrt{b_{j}(a_{i}+g_{j})}(\lambda_{1}+c_{i}\mu _{1})-a_{i}g_{j}\left(\lambda_{2}+d_{j}\mu_{2}\right)}\right]\\ &+\frac{\left[\sqrt{b_{j}(a_{i}+g_{j})}(\lambda_{1}+c_{i}\mu_{1})-a_{i}g_{j} \left(\lambda_{2}+d_{j}\mu_{2}\right)+a_{i}\sqrt{\lambda_{2}+d_{j}\mu_{2}} \right]^{2}}{b_{j}(a_{i}+g_{j})^{2}}\\ &-\frac{1}{2}\left(\log_{2}e+1+\log_{2}\ln 2\right)\end{array} \tag{24}\]  
when \(p_{s,i}^{\star}>0\) and \(b_{j}\left(\lambda_{1}+c_{i}\mu_{1}\right)>g_{i}\left(\lambda_{2}+d_{j}\mu_{2}\right)\) hold;  
\[\varphi_{i,j}=\log_{2}\left[\sqrt{\frac{g}{\lambda_{1}+c_{i}\mu_{1}}}\right] +\frac{\lambda_{1}+c_{i}\mu_{1}}{g}-\frac{1}{2}\left(\log_{2}e+1+\log_{2}\ln 2\right) \tag{25}\]  
when \(p_{s,i}^{\star}>0\) and \(b_{j}\left(\lambda_{1}+c_{i}\mu_{1}\right)\leq g_{i}\left(\lambda_{2}+d_{j}\mu _{2}\right)\) hold; and \(\varphi_{i,j}=0\) when \(p_{s,i}^{\star}=0\) holds. Similarly, the problem (23) is a typical linear assignment problem, which has been solved in [26]. Finally, dual variables have to be searched iteratively to satisfy the power and interference threshold constraints.
## 4 Study Results
### Differential Speed-Limit (DSL) Results
#### 4.2.2 Simulated Safety Results  
Table 13 shows the conflict ratio (\(\rho\)) for the total simulated conflicts for different TTC thresholds for the USL strategy, with speed limits of 80 and 90 km/h for PCs and 60 km/h for HVs. The results in Table 13 show that the \(\rho\) value for almost all scenarios (with both speed limits with different traffic volumes) with different TTC thresholds is less than 1.0, which indicates a reduction in the simulated conflicts and, hence, a reduction in crashes. There are only a few values at TTC \(\leq\) 2.50 s that show a statistically significant value of \(\rho\) \(>\) 1.0 at a traffic volume of 1250 vehicle/h, which indicates an increase in the simulated conflicts. These instances should not be viewed as safety drops, as mentioned earlier.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \multirow{2}{*}{**V**} & \multirow{2}{*}{**\%HV**} & \multicolumn{3}{c}{**SL = 80 km/h for PCs and 60 km/h for HVs**} & \multicolumn{3}{c}{**SL = 90 km/h for PCs and 60 km/h for HVs**} \\ \cline{3-8}  & & **TTC \(\leq\) 2.50** & **TTC \(\leq\) 1.50** & **TTC \(\leq\) 0.50** & **TTC \(\leq\) 2.50** & **TTC \(\leq\) 1.50** & **TTC \(\leq\) 0.50** \\ \hline
250 & & 0.09 (0.04) & 0.00 (0.00) & 0.00 (0.00) & 0.05 (0.02) & 0.0
## 2 Materials and Methods
### Effect of PACT on Biofilm  
To evaluate the effect of PACT on _P. aeruginosa_ biofilm, viable counts were determined after single, double and triple photoinactivation. For this purpose, biofilms were grown in 96-well microtiter plates for 24 h, as described previously (2019). The medium was removed, and the wells were washed gently with PBS in order to remove planktonic cells. Subsequently, 200 \(\mu\)L aliquots of Ps solution (group Ps+) were added. The samples were incubated for 15 min and irradiated by LED panel (group L+) for 20 min. Next, the solution containing Ps was removed, and the first set of samples was tested for determination of the viable cells of bacteria in biofilm, while another 200 \(\mu\)L aliquot of Ps solution was added to the remaining two sets of samples. The procedure was repeated twice, and the viable cells of bacteria in biofilm were determined after the second and the third treatment. To determine the number of viable cells, the biofilm was scraped carefully and homogenized. Samples were diluted with PBS in series, plated on the TSA plates and incubated 20 h at 36 \(\pm\) 1 \({}^{\circ}\)C. Simultaneously, the sets of samples in the dark condition (group L-) were tested. Irradiated and non-irradiated viable bacteria in samples and the reductions of bacterial cell populations were calculated as described above (Section 2.5).
# Non-Invasive Glucose Measurement Technique Based on Time-of-Flight
## 4 Results  
### Measurement Post Processing  
As has already been mentioned, the sensor used provides data in the form of a binary stream. This data are captured and transferred to a PC as a single file to simplify firmware development on the hardware side. On the host PC, the binary file is converted to four separate binary arrays that include the sensor phase and amplitude data. These were initially converted to simple grayscale images by mapping all possible sensor values to the 8 bit grayscale image space (with the expected loss of information) in order to verify correct sensor operation and communication. This was achieved by commanding the sensor to switch to the integrated testing mode, which switches the incoming data with a predictable array of increasing numbers (a simple binary counter). After successful verification of operation, the test mode functionality was disabled.  
To decrease possible noise from the sensor, multiple samples were acquired for each sample. Each measurement from the sensing element consists of 4 arrays of 4800 cells. The cells of each array correspond to a pixel of the sensor array while the arrays represent the four different data types that the senor provides. These are the amplitude of the signal received, flags that provide data for the operation of the relevant pixel (e.g., if it has been saturated), ambient light data (in case of operation in an area with near infrared sources), and the phase data of the received signal. The last one is the data used in this work. This is the phase difference of the captured signal to the reference signal (as created by modulating the LED driving current) with higher values representing higher difference and thus greater distance traveled.  
The multiple recorded samples were averaged to produce a single array for each measurement. The data produced thus were in turn further filtered using a moving window  
Figure 6: High-level diagram of the developed circuit around to operate the sensor.  
averaging function with a sample size of 1000 to remove noise. This value was selected so as to prevent excessive smoothing, but also remove spikes of noise in the produced data. The moving average filtering algorithm was selected over other possible algorithms (for example weighted averaging) due to several reasons--mainly the following:  
* The application allowed for a trade-off, between preserving fine details and simplicity. As a mean value was required, fine detail was not necessary.
* As the data were a 1D array from a 2D image, there were a number of spikes and anomalies (e.g., due to local 2D irregularities that were converted to periodic irregularities in the array). The moving average filter effectively handled their removal.
* At this stage of sensor development and to simplify testing, the moving average filter's straightforward implementation was considered an ideal choice.  
Finally, the sensor provided amplitude data was also compared between different measurements and found to differ within the detection limits of the sensor (\(\pm 2 LSB\)).
## 3 Results and Discussion
### Comparison of the Proposed MW-FLR Method with Reference Methods  
To compare the proposed MW-FLR with pre-validated reference methods, samples from copiktra capsules having varying DUV concentrations (100-800 ng/mL) and plasma samples spiked with the same concentrations of DUV were analyzed by the proposed MW-FLR method and reference methods. The reference methods were microwell-spectrophotometric assay (MW-SPA) [17] and HPLC with fluorescence detection (HPLC-FD) [21] for analysis of copiktra capsules and plasma samples, respectively. The values obtained by both MW-FLR, and reference methods were correlated very well with each other (Figure 7). The regression analysis of the results showed a good agreement between the results obtained by the two methods, as evident from the obtained good correlation coefficients (Figure 7). This indicated the validity of the proposed MW-FLR for the accurate determination of DUV in copiktra capsules and plasma samples.
## 3 Classifications of Routing Protocols in UAV Networks
### Routing Based on Network Structure  
Compared with traditional network architectures, the design of routing protocols in UAV networks faces many challenges due to the characteristics of the network such as high mobility and continuous changes in network structure [14]. Based on the operational characteristics, routing protocols on network structure can be divided into static routing protocols, proactive routing protocols, reactive routing protocols, and hybrid routing protocols.
## 2 Methods
### Matrices for Storing Model Data  
#### 2.2.1 Adjacency Matrix  
In this study, every RSCN information is stored using an adjacency matrix (AM).  
\[AM_{ij}^{(m)}=\begin{cases}1,\;|\;\;\Big{\langle}V_{i}^{(m)},\;V_{j}^{(m)} \Big{\rangle}\in E^{(m)},\\ 0,\;|\;\;\Big{\langle}V_{i}^{(m)},\;V_{j}^{(m)}\Big{\rangle}\
otin E^{(m)}, \end{cases} \tag{3}\]  
where  
* \(\Big{\langle}V_{i}^{(m)},\;V_{j}^{(m)}\Big{\rangle}:\) The directed connection from node \(V_{i}^{(m)}\) to node \(V_{j}^{(m)}\), where \(i\
eq j\), and \(V_{i}^{(m)},\;V_{j}^{(m)}\in V^{(m)}\);
* If the value of \(AM_{ij}^{(m)}\) is 1, it indicates the existence of a direct edge \(\Big{\langle}V_{i}^{(m)},\;V_{j}^{(m)}\Big{\rangle}\) connecting node \(V_{i}^{(m)}\) to node \(V_{j}^{(m)}\).
## 3 Results
### Factor Contributions and Hazard-Inducing Environmental Characteristics Across Zones
#### 3.2.1 Zonal Factor Characteristics  
As illustrated in Figure 4, the CLD hazard-inducing environments across Zones I-VIII display two primary patterns, geomorphological-structural dominance and climate-engineering geology co-driving, characterized by pronounced regional differentiation and multi-factor coupling. The specific characteristics of each zone are as follows:  
Figure 3: RMSE accuracy evaluation for individual zones (I–VIII), where red curves denote zone-specific RMSE fitting trends. With increasing feature count, the curves stabilize, indicating improved model robustness. Subzones: I, Alpine Valley Freeze–Thaw Tectonic Zone; II, Alpine Canyon Tectonic-Erosional Zone; III, Alpine Freeze–Thaw Tectonically Active Zone; IV, Faulted Basin Weather Fragmentation Zone; V, Permafrost–Weathered Rock Belt; VI, Permafrost–Nival Erosion Zone; VII, Alpine Hard Rock Freeze–Thaw Debris Zone; VIII, Alpine Lake Basin Saline Soft Rock Zone.  
Zone I: Alpine Valley Freeze-Thaw Tectonic Zone: Dominated by geological structures (30.884%) and precipitation (26.482%) as the principal hazard-inducing factors.  
Zone II: Alpine Canyon Tectonic-Erosion Zone: Primarily controlled by geological structures (30.531%) and terrain geomorphology (27.034%), with precipitation (25.551%) exerting a synergistic influence.  
Zone III: Alpine Freeze-Thaw Tectonically Active Zone: Precipitation (33.134%) is the leading hazard-inducing factor, operating in conjunction with geological structures (24.705%) and engineering geological conditions (22.13%) in a co-driven mechanism.  
Zone IV: Fauulted Basin Weathered Fragmentation Zone: Engineering geological conditions (27.454%) and terrain geomorphology (26.889%) serve as the dominant factors, supplemented by geological structures (23.844%) and precipitation (21.814%) through a compound interaction.  
Zone V: Permafrost-Weathered Rock Belt: Jointly driven by precipitation (30.164%) and engineering geological conditions (26.34%), with terrain geomorphology (24.627%) further contributing to the hazard-inducing environment.  
Zone VI: Permafrost-Nival Erosion Zone: Terrain geomorphology (29.88%) and precipitation (26.885%) are the primary controlling factors, co-driven by engineering geological conditions (23.353%).  
Zone VII: Alpine Hard Rock Freeze-Thaw Debris Zone: Engineering geological conditions (42.077%) represent the core hazard-inducing factor, while precipitation (26.731%) contributes to the triggering mechanism.  
Zone VIII: Alpine Lake Basin Saline Soft Rock Zone: Dominated by engineering geological conditions (31.868%) and precipitation (28.52%), with terrain geomorphology (20.218%) playing a supporting role through spatial coupling effects.
# Energy Consumption Model for Sensor Nodes Based on LoRa and LoRaWAN
## 6 Numerical Results and Discussions
### Effect of Microcontroller Frequency in Sensor Node Lifetime  
To show the effect of the microcontroller operating frequency \(f_{MCU}\) on the sensor node autonomy, we refer to Figure 22 (the voltage supply is set to 3.3 V) [33]. We note that the energy consumption of the node depends on the microcontroller speed (it increases with frequency). In fact, the higher the microcontroller frequency is, the lower the processing time is (meaning that the processing time \(T_{proc}(f_{MCU})\) decreases) which causes the decrease of the microcontroller time duration \(T_{MCU}(f_{MCU})\)  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Mode** & **Total Consumed Energy Per Transmission (m)** & **Sensor Node Autonomy (Month)** \\ \hline Mode 1 & 115 & 1.12 \\ Mode 2 & 14.7 & 8.2 \\ Mode 3 & 1.2 & 54.4 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Comparison between different LoRaWAN modes (using Scenario 2).  
Figure 21: Battery life vs. probability of having Scenario 3.  
but the higher the amount of the microcontroller power \(P_{ON}(f_{MCU})\) is. Consequently, the node autonomy decreases with increasing the MCU frequency.
###### Abstract  
The large amount of features recorded from GPS and inertial sensors (external load) and well-being questionnaires (internal load) can be used together in a multi-dimensional non-linear machine learning based model for a better prediction of non-contact injuries. In this study we put forward the main hypothesis that the use of such models would be able to inform better about injury risks by considering the evolution of both internal and external loads over two horizons (one week and one month). Predictive models were trained with data collected by both GPS and subjective questionnaires and injury data from 40 elite male soccer players over one season. Various classification machine-learning algorithms that performed best on external and internal loads features were compared using standard performance metrics such as accuracy, precision, recall and the area under the receiver operator characteristic curve. In particular, tree-based algorithms based on non-linear models with an important interpretation aspect were privileged as they can help to understand internal and external load features impact on injury risk. For 1-week injury prediction, internal load features data were more accurate than external load features while for 1-month injury prediction, the best performances of classifiers were reached by combining internal and external load features.  
Keywords:injury prediction; training load monitoring; non-linear machine learning; well-being questionnaires; one-month and one-week time horizons; sport science +
Footnote †: journal: Applied Sciences
# From Complex Modulus E* to Creep Compliance D(t): Experimental and Modeling Study
## Highlights  
* The use of rheological models is a very simple and good method to predict creep compliance D(t) from complex modulus E(\(\omega\)).
* Creep compliance from direct tensile and direct compression tests are the same and are more representative to the field.
* The IDT creep test configuration allow to measure parallel creep compliance which is not representative with thermal cracks observed in the real roads.
* Direct creep compliance tests are easier to perform comparing to the IDT configuration proposed by AASHTO T322-07.
# Encoder-Decoder Structure Fusing Depth Information for Outdoor Semantic Segmentation
## 2 Materials and Methods
### Loss Function  
We applied the most commonly used cross-entropy function in the field of semantic segmentation as the loss function, which was used to measure the difference between the real and predicted probability distributions [16; 17]. The segmentation problem can be viewed as a multiclassification problem. Therefore, similar to the classification task, we used the sigmoid function to output a probability value representing the probability of a positive sample at the last layer of the semantic segmentation model. The probability that the network outputs positive samples is:  
\[\overset{\wedge}{\underline{y}}=P(y=1|x) \tag{2}\]  
The probability of outputting negative samples is:  
\[1-\overset{\wedge}{\underline{y}}=P(y=0|x) \tag{3}\]  
From the perspective of maximum likelihood, we integrated Equations (2) and (3) to obtain  
\[P(y|x)=(\overset{\wedge}{\underline{y}})^{y}\cdot\left(1-\overset{\wedge}{ \underline{y}}\right)^{1-y} \tag{4}\]  
To avoid changing the monotonicity of Equation (4), we took the logarithms at both ends of Equation (4):  
\[\log P(y|x)=\log(\overset{\wedge}{\underline{y}}^{y}\cdot\left(1-\overset{ \wedge}{\underline{y}}\right)^{1-y})=y\log\overset{\wedge}{\underline{y}}+(1-y )\log(1-\overset{\wedge}{\underline{y}}). \tag{5}\]  
The greater the probability of the positive samples, the higher the accuracy of object segmentation in the foreground. Finally, we obtained the cross-entropy equation as follows:  
\[L=-\frac{1}{N}\underset{i=1}{\overset{N}{\sum}}y^{(i)}\log\overset{\wedge}{ \underline{y}}^{(i)}+\left(1-y^{(i)}\right)\log(1-\overset{\wedge}{\underline {y}}^{(i)}). \tag{6}\]  
For the multiobjective segmentation problem, the cross-entropy loss function can be expressed as [18; 19; 20]:  
\[L=-\underset{c=1}{\overset{M}{\sum}}\underset{o=1}{\overset{N}{\sum}}y_{o,c }\log(p_{o,c}) \tag{7}\]  
where \(N\) is the total number of pixels, \(o\) is a pixel index, \(M\) is the total number of categories, \(y_{o,c}\) is the probability that ground truth pixel \(o\) belongs to category \(c\), and the value is 0 or 1, and \(p_{o,c}\) is the probability that pixel \(o\) is predicted to be true category \(c\), which is the output of the semantic segmentation model.