esa-sceva/satcom-chunk-collection huggingface dataset:
# A Bandwidth-Efficient Dissemination Scheme of Non-Safety Information in Urban VANETs +
## 3 Design of a Scheme for Urban Dissemination
### Broadcast Suppression Mechanism
#### 3.1.2 Angle-Based Adaptation for Cities  
As a different alternative, we propose that vehicles pay attention to the angle of the previous sender with respect to their own trajectory. We assume that if it is more or less close to a right angle to either side, the message comes from another street. A problem of urban scenarios is that the signal may bounce of and be redirected by buildings, parked cars, street signs, etc. Therefore, learning the reception angle from the antenna may be misleading. To avoid this, we calculate it based on the other's reported location and the receiver knowledge of its own trajectory. We will use two threshold angles, \(\alpha_{min}\) and \(\alpha_{max}\), in order to set a range for the condition evaluation. If the reception angle falls between the two of them, the vehicle is considered to be at an intersection. It cannot determine the distance to the intersection center, unlike in the map-based adaptation, so next, it waits a random amount of time, \(t_{j}^{\prime}\), from the distribution in Equation (3).  
\[t_{j}^{\prime}\sim U(0,T_{j}) \tag{3}\]  
Again, the first vehicle to forward inhibits the rest at the intersection to do the same. When using the angle-based method, it will need to use a generic threshold distance, \(d_{j}\). If the other vehicle is closer than this distance, we assume that it is located at the same intersection.
## 3 Numerical Implementations and Algorithms
### Additional Solid Routines


## Appendix A Chapman-Enskog Expansion for Static Solid Equilibrium

In order to keep the second order accuracy, the following substitution is performed [48]:

\[\frac{1}{\widehat{\omega}} =\frac{1}{\omega}+\frac{\Delta t}{2}, \tag{18}\] \[\widehat{f}_{i}(\mathbf{x},t) =f_{i}-\frac{\Delta t}{2}\overline{\Omega_{i}}(t)\] \[=f_{i}+\frac{\Delta t\omega}{2}\Big{(}f_{i}(\mathbf{x},t)-f_{i}^{(eq )}(\mathbf{x},t)\Big{)}+\frac{\Delta t}{2}g_{i}(\mathbf{x},t)\] (19) \[=f_{i}+\frac{\Delta t\widehat{\omega}}{2-\widehat{\omega}\Delta t }\Big{(}f_{i}(\mathbf{x},t)-f_{i}^{(eq)}(\mathbf{x},t)\Big{)}+\frac{\Delta t}{2}g_{i}( \mathbf{x},t),\]

which leads to the discretized version of the BE, i.e., the LBGKE expressed by:

\[\widehat{f}_{i}(\mathbf{x}+\xi_{i}\Delta t,t+\Delta t) =\widehat{f}_{i}(\mathbf{x},t)-\Delta t\widehat{\omega}\Big{(} \widehat{f}_{i}(\mathbf{x},t)-f_{i}^{(eq)}(\mathbf{x},t)\Big{)}-\Delta t\bigg{(}1- \frac{\widehat{\omega}\Delta t}{2}\bigg{)}g_{i}(\mathbf{x},t)+\mathcal{O}\Big{(} \Delta t^{3}\Big{)}. \tag{20}\]

Then, using the Taylor expansion of \(\widehat{f}_{i}\) in Equation (20), we get:

\[\widehat{f}_{i}(\mathbf{x}+\xi_{i}\Delta t,t+\Delta t) =\widehat{f}_{i}(\mathbf{x},t)+\sum_{m=1}^{\infty}\frac{1}{m!}\Big{[} \Delta t\partial_{t}+\xi_{i}\Delta t\cdot\
abla_{\mathbf{x}}\Big{]}^{m}\widehat{f }_{i}(\mathbf{x},t) \tag{21}\] \[=\widehat{f}_{i}(\mathbf{x},t)-\Delta t\widehat{\omega}\Big{(} \widehat{f}_{i}(\mathbf{x},t)-f_{i}^{(eq)}(\mathbf{x},t)\Big{)}-\Delta t\bigg{(}1- \frac{\widehat{\omega}\Delta t}{2}\bigg{)}g_{i}(\mathbf{x},t)+\mathcal{O}\Big{(} \Delta t^{3}\Big{)}.\]

Therefore, the LBGKE becomes:

\[\sum_{m=1}^{\infty}\frac{1}{m!}\Big{[}\Delta t\partial_{t}+\xi_{i}\Delta t \cdot\
abla_{\mathbf{x}}\Big{]}^{m}\widehat{f}_{i}(\mathbf{x},t)=-\Delta t\widehat{ \omega}\Big{(}\widehat{f}_{i}(\mathbf{x},t)-f_{i}^{(eq)}(\mathbf{x},t)\Big{)}-\Delta t \bigg{(}1-\frac{\widehat{\omega}\Delta t}{2}\bigg{)}g_{i}(\mathbf{x},t). \tag{22}\]

The modal decomposition is introduced by the time-scales (\(\epsilon\)) power series decomposition such as:

\[\Delta t\partial_{t} =\sum_{k=1}^{\infty}\epsilon^{k}\partial_{t}^{(k)}, \tag{23}\] \[\Delta t\
abla_{\mathbf{x}} =\epsilon^{1}\
abla_{\mathbf{x}}\,{}^{(1)},\] (24) \[\widehat{f}_{i}(\mathbf{x},t) =f_{i}-\frac{\Delta t}{2}\overline{\Omega_{i}}(t)\]0 (25) \[\widehat{f}_{i}(\mathbf{x},t) =f_{i}-\frac{\Delta t}{2}\overline{\Omega_{i}}(t)\]1

The source term is local, so its perturbation of the distribution function will be brought through time. furthermore, the source term \(g_{i}\) is limited to the first order of perturbation here. The previous equation is true for every order \(\epsilon^{k}\).
# Enhancing Space Management through Digital Twin: A Case Study of the Lazio Region Headquarters
## 3 Results and Discussion
### Digital Transformation in Modern Work Environments
The successful integration of these technologies requires data exchange with open formats and consistent information to ensure an accurate and efficient flow of information between systems. Industry Foundation Classes (IFC) is an open, international standard for BIM data exchange, providing a common data format that facilitates the integration of various systems and technologies [97]. By defining a standardized schema for building information, IFC ensures that data can be accurately shared and interpreted across different platforms, enhancing interoperability among BIM, IoT, AI, and DT systems. However, despite its benefits, the implementation of IFC can be complex, as it requires the alignment of data models and protocols across diverse systems. The effective utilization of IFC is crucial for achieving seamless data integration and enabling a holistic approach to building management and operations, ultimately improving efficiency and performance in the AECO sector. In some buildings, an additional challenge is the wait time for elevators, which can significantly impact occupant satisfaction and building efficiency. The integration of advanced technologies can help optimize elevator operations by analyzing usage patterns and dynamically adjusting schedules to reduce wait times. This further highlights the importance of effective data integration and management to address various operational issues within smart buildings.
## 4 Experimental Setup
### Evaluation Metrics  
The evaluation metrics presented in the following paragraphs describe the approach used to quantify the obtained values to support our performance comparison. The following metrics are discussed: classification accuracy, mean accuracy, best accuracy and the standard deviation fitness obtained using Equations (14)-(16).  
1. Classification accuracy (CA): this computes the accuracy of classifier \(clf\) with dataset \(X\) and label \(Y\), as described in Equation (14): \[CA=clf(X,\ Y)\] (14)
2. Mean accuracy (MA): this computes the mean of all classification accuracies obtained after a certain number of runs on a given algorithm, where \(acc_{i}\) is the accuracy obtained during iteration \(i\) after \(N\) iterations and all accuracy values \(acc\) obtained for \(N\) times, as described in Equation (15): \[Mean_{acc}=\frac{1}{N}\sum_{i=0}^{N}CA_{i}\] (15)
3. Best Accuracy (BA): the best of all classification accuracies obtained after a certain number of runs, as described in Equation (16): \[best_{acc}=\max(CA)\] (16)
4. Average feature count (AFC): obtained by finding the average value for all numbers of selected features for all population groups \((PG)\), as described in Equation (17): \[AFC=\frac{1}{PG}\sum_{i=0}^{PG}fc_{i}\] (17)  
The following section presents the results of all experiments and a comparative analysis of the algorithms. Additionally, the findings derived from the results are highlighted.
## 2 Background
### Requirements for the Calibration Data Management and Data Formats
#### 2.3.2 Mass as an Example Measurand  
The differences that the standards, regulations, and guidelines of the different organizations may have can be pointed out by examining a singular physical quantity and the respective measurement methodologies. Harmonization is beneficial as differences in the calibration procedures can result in differences, e.g., in uncertainty evaluations [52]. Good examples of the harmonization of practices on a global level are the most recent revisions of the regulations and guidelines for mass and weighing, e.g., non-automatic weighing instruments.  
Standards covering the use of non-automatic weighing instruments include the European EN 45501:2015 \"Metrological aspects of non-automatic weighing instruments\" [53] and American NIST Handbook 44 \"Specifications, Tolerances, and Other Technical Requirements for Weighing and Measuring Devices\" [54].  
Regional guidelines for the calibration procedures for weighing instruments are published by the RMOs. In the case of non-automatic weighing instruments, these guidelines include, for example, Euramet Calibration Guideline No. 18 \"Guidelines on the Calibration of Non-Automatic Weighing Instruments\" [55], ASTM E898 \"Standard practice for calibration of non-automatic weighing instruments\" [56], SIM MWG7 \"Guidelines on the calibration of non-automatic weighing instruments\" [57], and JJF 1847 \"Calibration Specifications for Electronic Balances\" [58]. Harmonization of these guidelines has been based on the other organizations adapting their guidelines to commensurate with the EURAMET CG-18, which has also become better known in the Asian region, allowing the requirements to become a global de-facto standard [11].  
On the industrial level, the requirements for the weighing instruments and other measurement instruments are defined in the regional or national regulations for pharmaceutical products. In the US Pharmacopoia, the requirements for balances are covered in General Chapter 41 \"Balances\" [9], with supporting guidance given General Chapter 1251 \"Weighing on an Analytical Balance\" [59]. Respectively, in the European Pharmacopeia, the requirements for balances are covered in General Chapter 2.1.7 \"Balances for analyticalpurposes\" [10]. After the latest revision of the European Pharmacopoeia, the requirements are commensurate with the US Pharmacopoeia, which has been a welcome advancement towards the harmonization of regulations [11].
## 3 TDoA Method Experimental Validation
### Simulated DoA Accuracy versus SNR  
For the sake of a better comprehension of the experimental results, we report briefly the results of a set of simulations carried out by implementing the analytical model of Section 2.1 and applying the technique described in Section 2.3. During this analysis,  
Figure 15: Cumulative distribution function for range estimation error and positioning error; dashed curves are related to estimation in the 4.75–6.25 GHz frequency band.  
we considered both the SBA and the transmitting antenna as implemented of radiators characterized by the features described in Table 1 and with the radiation pattern of Figure 6. We adopted the \(awgn\{.\}\) function available in Matlab, which can estimate the incoming signal power and to add a proper level of white noise, such that the signal-to-noise ratio (SNR) reach a given value. The results of this analysis were conducted at different SNR levels, ranging from \(-15\) dB to \(10\) dB, and the corresponding CDF curves are reported in Figure 16. From the CDF curve associated to SNR = \(-15\) dB, we can recognize the shape already shown in Figure 14c; under a closer inspection, we see that the CDF values at errors \(10\) degree and \(20\) degree are in agreement for less than \(3\%\) with those experimentally derived. We can thus conclude that the overall SNR affecting the measurement is in the range of \(-15\) dB, and that the analytical model is capable to reproduce with accuracy of less then \(3\%\) the DoA experiments. We should not be surprised about the capability of the method to recover the DoA estimate with good accuracy, because in spite the large bandwidth adopted, the method introduces a process gain due to inverse FFT involved in the phase reconstruction.
# Reliability Assessment of Cyber-Physical Distribution Systems Considering Cyber Disturbances
## 4 Reliability Assessment of Distribution Network CPS
### Reliability Assessment Indices
#### 4.1.1 The Generalized System Average Interruption Frequency Index-\(G_{SAIFI}\)  
\(G_{\text{SAIFI}}\) reflects the frequency of load shedding due to control failures caused directly by physical component failures or through cyber disturbances. This index is defined as follows:  
\[G_{\text{SAIFI}}=\frac{\sum\limits_{l=1}^{n}n_{l}\Big{[}C_{\text{ph},l}+(1-p_{l} )C_{\text{in},l}+p_{l}C_{\text{ia},l}\Big{]}}{\sum\limits_{l=1}^{n}n_{l}}, \tag{19}\]  
where \(n\) represents the number of load points; \(n_{l}\) represents the number of users of the \(l\)-th load point; \(C_{\text{ph},l}\) denotes the number of load points shed due to the direct failure of physical components; \(C_{\text{in},l}\) means the number of load points shed due to control failure caused by the failure of information components; and \(C_{\text{ia},l}\) stands for the number of load points shed due to control failure caused by cyberattacks.
## 3 Results and Discussion
### Adsorption Efficiency of LPD Biomass
#### 3.2.5 Effect of Adsorbent Dosage  
The effect of sorbent dosage on the removal of Pb(II) was also evaluated under the following conditions: 0.1, 0.2, 0.5, 1, and 2 g adsorbent, 50 mL metal ion solution, 55 ppm initial ion concentration, 2 h agitation time, 150 rpm agitation speed, pH \(\sim\)6, and 25 \({}^{\circ}\)C adsorption temperature. From the results in Figure 4c, the highest adsorption capacity (\(q_{e}\), 14.2 mg/g) of Pb(II) was obtained at 0.1 g (the lowest experimental adsorbent dose); also, with increasing the dose from 0.1 to 0.5 g a noticeable rapid decrease in the adsorbent capacity, to 3.3 mg/g, was observed.. The decreasing behavior of adsorption capacity at higher adsorbent dosage (i.e., above 0.5 g) might be due to aggregation of the adsorption particles, preventing ions reaching the active sites on the adsorbent. However, it can be seen that, when further increasing the adsorption dosage above 0.5 g, the removal percentage of Pb(II) did not change significantly, due to the maximal possible aggregation that may be reached [35]. These findings are consistent with previous studies, in which the dosage is an important factor for effective adsorption [30; 35; 36]. In reference to the isolated compounds, the chemical composition of the powdered LPD plant may facilitate such aggregation, that is, by physical and hydrogen bonding interaction between, for example, amides, esters, and alcoholic functionalities, meaning that the active site occupation becomes less in the highly compacted bulky materials at higher dose.
## 3 5G and TSN Integrated Prototype for Resilience and High Reliability
### Setup of the Time-Sensitive Networking Environment  
TSN is a set of IEEE standards aimed at enhancing Ethernet to support time-critical applications in industrial and automotive environments [29; 30]. TSN aims at four different functions: Time synchronization, high availability and reliability, bounded latency, and resource management. Part of the set is IEEE 802.1 CB-FRER [31], a standard that provides redundancy and fault tolerance in Ethernet networks by sending replicated frames and eliminating any redundant copies of the frame to enhance communication reliability as shown in Figure 3. To integrate the 5G mid-band deployment and the 5G mmW system deployment in the TSN network, a layer 2 to layer 3 tunnel has been used. The 5G URLLC testbed supports layer 2 traffic natively.
## 3 Results  
### Trained Network Model Evaluation  
The BRAU-Net++ neural network was trained with 447 sagittal T1- and T2-weighted MRI series with segmentation masks for nine classes over 400 epochs. The neural network achieved convergence and produced a trained model that was later used to generate segmentations of the vertebrae and spinal canal. The performance of the network was evaluated using DSC, accuracy, precision, recall and IoU for each of the nine classes for which the prediction masks were created. Table 1 shows the result of the evaluation metrics.  
The performance of the BRAU-Net++ network can be directly compared with that of the nnU-Net neural network, as both networks were trained with the same dataset. The results of the DSC evaluation metric for nnU-Net are 0.92 \(\pm\) 0.05 for the vertebrae and 0.92 for the spinal canal. The average DSC value for BRAU-Net++ for the vertebrae is 0.914 \(\pm\) 0.007, which is in the range of the values reported by nnU-Net. The DSC value for the spinal canal is 0.902 and thus below the result of the nnU-Net DSC score. Figure 4 shows examples of original images, ground truth segmentation and predictions for individual images from T1- and T2-weighted series.  
Even though the trained model performed well according to the evaluation metrics presented in Table 1, for some of the 166 series data chosen for VBQ determination, a mixing of segmentation labels is present in the prediction provided by the trained model. An example of segmentation label mixing is shown in Figure 5.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Class** & **DSC** & **Accuracy** & **Precision** & **Recall** & **IoU** \\ \hline
1–L1 & 0.907 & 0.999 & 0.922 & 0.892 & 0.829 \\ \hline
2–L2 & 0.917 & 0.999 & 0.927 & 0.907 & 0.846 \\ \hline
3–L3 & 0.917 & 0.999 & 0.928 & 0.906 & 0.847 \\ \hline
4–L4 & 0.912 & 0.999 & 0.923 & 0.902 & 0.838 \\ \hline
5–L5 & 0.913 & 0.999 & 0.917 & 0.908 & 0.839 \\ \hline
6–Th10 & 0.915 & 0.999 & 0.907 & 0.923 & 0.843 \\ \hline
7–Th11 & 0.926 & 0.999 & 0.927 & 0.926 & 0.862 \\ \hline
8–Th12 & 0.904 & 0.999 & 0.905 & 0.904 & 0.825 \\ \hline
9–Spinal canal & 0.902 & 0.998 & 0.897 & 0.907 & 0.821 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The evaluation metrics of the trained model.  
Figure 4: (**a**) Single mid-sagittal T1-weighted (**upper**) and T2-weighted (**lower**) image from a randomly chosen series. (**b**) Ground truth segmentations overlayed on the corresponding image from (**a**). (**c**) Prediction segmentation images of the trained neural network model. Different colors represent each of the 9 segmentation classes, which are labeled 1–9 in the segmentation masks.  
Figure 5: An image slice example showing mixing of labels (green/red and blue/yellow). Different colors represent each of the 9 segmentation classes, which are labeled 1–9 in the segmentation masks.
# Continuous Space Wireless Communication Tower Placement by Hybrid Simulated Annealing
## 4 Hybrid Simulated Annealing Algorithm
### Local Optimization  
#### 4.6.1 Motivation  
Initially, we developed a conventional SA algorithm for the wireless communication tower placement problem. However, when conducting experiments on the conventional SA, we observed that its exploration ability is great, but its exploitation ability is very poor, as in the process of annealing, the conventional SA could find an area where a number of users were clustered but could not find an optimal location for a wireless communication tower placement to cover maximally those users in the area. Figure 2 illustrates a typical solution found using the conventional SA algorithm.  
It can be seen from the figure that the conventional SA explored an area where five users are clustered. However, the solution found by the conventional SA algorithm covers only three of the five users. If we move the location of the wireless communication tower to the location illustrated in Figure 3, the wireless communication tower will cover all five users. This indicates that the exploitation capacity of the SA needs to be improved. Thus, it is desirable to develop a local optimization algorithm that can find an optimal or a near-optimal solution when the conventional SA algorithm explores a new search area. The local optimization algorithm can be embedded into the conventional SA to enhance the conventional SA's local exploitation ability.  
Figure 3: The ideal location of the wireless communication tower.  
Figure 2: A wireless communication tower location found by the conventional SA. In this figure, a user is marked by a ‘\(\circ\)’, a wireless communication tower is represented by a ‘\(\times\)’, and the coverage of a wireless communication tower is represented by a large broken line circle.
## 2 Materials and Methods
### Antioxidant Activity Measured by ABTS and DPPH Assays  
Trolox equivalent antioxidant capacity (TEAC) was determined according to the procedure described by [PERSON] et al. [34] with minor modifications. ABTS (2,2-azino-bis-[3-ethylbenzothazoline 6-sulphonic acid]) was dissolved in water to a 7 mM concentration. ABTS radical cation was produced, allowing the reaction of the ABTS stock solution with potassium persulfate (2.45 mM, final concentration) for 12-16 h in the dark and at room temperature before use. ABTS radical cation solution was diluted with ethanol to an absorbance of 0.70 \(\pm\) 0.02 at 734 nm (UV/Vis spectrometer Lambda Bio20, PerkinElmer, Inc., USA). TEAC values of the extracts were calculated, using a Trolox standard curve (0-15 \(\upmu\)M), based on the percentage inhibition of absorbance at 734 nm and expressed in \(\upmu\)mol of Trolox equivalents (\(\upmu\)mol TE)/g sample on dry matter (dm).  
DPPH assay (2,2\({}^{\prime}\)-diphenyl-1-picrylhydrazyl radical scavenging capacity assay) was determined according to the procedure of [PERSON] et al. [32] with some modifications. DPPH radical solution, having an absorbance value of 0.80 at 525 nm, was prepared daily by dissolving 5 mg of DPPH in 100 mL of a methanol/water mixture (50:50, \(v/v\)). For the determination of the DPPH scavenging activity, 20 \(\upmu\)L of each sample was added to 4.98 mL of DPPH solution. The reaction mixture was incubated at 30\({}^{\circ}\) C in the dark for 30 min, and then the absorbance at 517 nm was measured. DPPH values of the extracts were calculated, using a Trolox standard curve, based on the percentage inhibition of absorbance at 525 nm and expressed in \(\upmu\)mol of Trolox equivalents (\(\upmu\)mol TE)/g sample on dry matter (dm).
## 3 Results and Discussion
### VI Performance
Consequently, results are more sensitive to remaining noise in the data after sensor calibration than the results for VIs calculated from NIR and VIS bands with large differences in reflectance. Nonetheless, TBVI\({}_{\text{b899/b953}}\) exhibits a trend to more linearity than both NDVI and TSAVI (Figure 16c). Although developed for broad MODIS bands with the respective optimized coefficients to reduce impacts of soil and aerosol, EVI is the best predictor for fresh phytomass (Figure 16d). As reported in literature [4; 37; 42] the relationship is more linear and the range of values is wider (0.43) than observed for NDVI (0.25) and TSAVI (0.22). The RMSE and MRE for fresh phytomass using EVI are 193 g: m\({}^{-2}\) and 11%, respectively.