esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results  
### Thin-Film Structure  
Figure 1 shows typical optical microscopy pictures from the 480 nm-thick HfO\({}_{2}\) layers deposited with different voltage and power levels of the assisting ion beam source. Without extraction of the ion beam (U\({}_{\rm assist}\) = 0 V), the film exhibits high-quality continuous nearly defect-free state (Figure 1a). With beam extraction (U\({}_{\rm assist}\) > 0 V), the films show defects and start to crack or delaminate whereas the defect density increases with increasing voltage and power (Figure 1b,c). After a certain threshold, the film shows nearly complete blistering, resulting in a total failure of the coating (Figure 1d). In this state, the thickness monitoring as well as the ex situ thickness measurement by profilometry was highly inaccurate due to the blistering.  
In order to estimate the refractive index and the absorbance, we performed ellipsometry measurements. Figure 2a shows the results as a function of beam voltage of the assisting ion beam source for a reference wavelength of 633 nm. The initial refractive index without extracting the assisting ion beam is about 2.05, with an extinction k of about 0.0017. With extraction above a U\({}_{\text{assist}}\) = 250 V of the ion beam at the assisting source, a decrease in the refractive index is observed. In the case of the absorption, an increase with increasing beam voltage is observed. This is in contrast to the Ta\({}_{2}\)O\({}_{5}\), where the best parameters are achieved at intermediate assist voltage values [15]. For the highest applied ion beam voltage U\({}_{\text{assist}}\), the refractive index is around 1-2% smaller and absorption is approximately 50% higher, whereas an influence due to the delamination cannot be excluded.  
In Figure 2b, the grazing incidence XRD measurements of the 480 nm-thick HfO\({}_{2}\) single layers are shown for different assisting ion beam voltages. The films prepared with  
Figure 1: The state of the HfO\({}_{2}\) films for different assisting source voltages (**a–d**): 0, 250, 575, and 600 V, respectively. At low ion energies, only an increase in the large-scale defect concentration is observable (**b**); however, higher voltages lead to partial delamination or complete blistering (**c,d**).  
Figure 2: (**a**) The refractive index (n) and the extinction coefficient (k) at 633 nm as a function of the assist source voltage. The dashed lines are a 4 th order polynomial fit and linear fit of the data, respectively. (**b**) Grazing incidence XRD on the HfO\({}_{2}\) layers produced by different assisting beam voltage.  
IBS with the assisting source turned off appear almost X-ray-amorphous. With increasing voltage of the assisting source, the monoclinic HfO\({}_{2}\) peaks become clearly visible.
# Sigfox and LoRaWAN Datasets for Fingerprint Localization in Large Urban and Rural Areas
## 2 Low Power Wide Area Networks
### Nb-IoT  
In 2016, the 3 rd Generation Partnership Project (3 GPP) has introduced the NB-IoT standard. One of the main advantages of this new standard is its compatibility with traditional cellular networks. A 200 kHz GSM carrier can be re-purposed as an NB-IoT carrier. Also, NB-IoT can be implemented in the guardband of the LTE carrier or inside an LTE carrier by assigning a 180 kHz Physical Resource Block (PRB). Numerous properties such as MAC protocols are inherited from LTE. For instance, Orthogonal Frequency Division Multiple Access (OFDMA) is used for downlink and Single Carrier Frequency Division Multiple Access (SC-FDMA) is implemented for uplink. As a result, data rates are limited to 250 kbps for downlink, and 20 kbps for uplink [11]. [PERSON] et al summarize a few flaws of NB-IoT [2]. Firstly, not all messages are acknowledged due to limited downlink capacity. Secondly, implementation of packet aggregation introduces additional latency which is undesirable for time-critical communication. Thirdly, the performance of NB-IoT may decline when the network is being heavily used for voice- and data traffic. Lastly, NB-IoT is a very recent LPWAN technology. Thus, the amount of commercial applications has been limited up to now. More research has to be conducted in order to obtain more knowledge about real world performance and battery life of and NB-IoT end-device.  
In order to locate an NB-IoT end device, Observed Time Difference of Arrival (OTDoA) localization could be used. A number of synchronized base stations transmit a Positioning Reference Signal (PRS), which is received by the end device. The end device forwards the TOA per transmitting base station to a localization server, where the difference between these TOA's and the PRS is used to calculate the estimated location of the end device [18].  
In the remainder of this paper, we describe how our Sigfox and LoRaWAN datasets were collected and analyzed. Future work includes a similar measurement campaign and analysis for NB-IoT messages.
# Volumetric Techniques for Product Routing and Loading Optimisation in Industry 4.0: A Review
## 4 Volumetrics
### Balancing, Priority, Fragility, or Other Constraints  
Multidimensional bin packing is a critical logistics challenge that requires efficient placement of items within containers, taking into account various constraints to ensure safety, stability, and overall efficiency during transportation. A synthesis of findings from 16 studies reveals a common thread of constraints that researchers consider in solving these complex packing problems.  
Balancing constraints are a fundamental consideration in many of the discussed papers. These constraints focus on achieving balanced weight distribution, volume distribution, or the distribution of centre of mass among containers or bins. Maintaining such equilibria is essential to prevent overloading and maintain stability during transport [17; 18; 19; 20].  
Priority constraints also play a pivotal role in scenarios where specific items demand special handling due to factors like fragility, value, or time-sensitivity delivery. These constraints guide the packing strategy, ensuring that high-priority items are placed with care and precision [16; 21; 22; 23].  
Breakability constraints are addressed in several papers, emphasising the need to protect fragile items during transportation. These constraints call for thoughtful arrangements and protective measures to minimise the risk of damage [12; 14; 15; 24].  
Stability constraints, especially in the context of logistics and shipping, ensure that items are placed in a way that prevents shifting or toppling during transit [14; 21; 24; 25]. Space utilisation constraints are another common theme across studies, where efficient space usage within containers is a primary goal, as this can contribute to increased stability.  
Item rotation or orientation constraints play a crucial role in multidimensional bin-packing problems, where items or boxes come in various shapes and sizes. These constraints pertain to how each box can be oriented or rotated within a container or bin; however, these can contribute to increased complexity [26; 27; 15; 22]. Other constraints like dimension, weight, and volume are considered in most studies to address the unique requirements of different scenarios. These constraints collectively form a comprehensive framework for tackling multidimensional bin-packing problems, highlighting the complexity and adaptability required to address real-world packing challenges [12; 15; 16; 28].
## 1 Introduction  
White light-emitting-diodes (LEDs) have more advantages, such as energy saving, long lifetime, environmental friendly and high efficiency, compared with fluorescent lamps [1; 2; 3]. At present, white LEDs can be obtained by combining near UV LED chips with blue/green/red tricolor phosphors, in order to obtain a higher efficiency white LED with appropriate color temperature and higher color-rendering index. This type of white LED is expected to dominate the market in the near future [4,5]. So it is important to find new tricolor phosphors for near UV LED chips.  
The phosphors for near UV LED chips should exhibit intense broad emission with broad excitation band in near UV range. Compared with the phosphors activated by some trivalent rare earth ions (such as Eu\({}^{3+}\)), Eu\({}^{2+}\) ions in many phosphors have broader emission with broad excitation band in near UV range [6, 7, 8, 9], which are assigned to the 4f\({}^{6}\)5d\({}^{1}\)\(\rightarrow\) 4f\({}^{7}\) transitions. In order to strengthen and broaden the absorption of phosphors in near UV range, one important approach is to introduce sensitizer to strengthen the excitation band in the phosphor. We consider that Bi\({}^{3+}\) is probably an eligible co-activator. On one hand, the introduction of Bi\({}^{3+}\) will influence their sub-lattice structure around the luminescent center ion. On the other hand, Bi\({}^{3+}\) is a very good sensitizer of luminescence in many hosts [10, 11, 12]. It can efficiently absorb the UV-light and transfer the energy to the luminescent center, then the excitation band would be broadened and the emission intensity of the luminescent center would be strengthened.  
Alkaline earth halo-silicates are well known good hosts for inorganic luminescent materials, due to their low synthesis temperature and high luminescence efficiency [9]. Alkaline earth chlorosilicate Sr\({}_{4}\)Si\({}_{3}\)O\({}_{8}\)Cl\({}_{4}\) can be served as the host of phosphors, which has an orthorhombic crystal structure [13]. The luminescent properties of Sr\({}_{4}\)Si\({}_{3}\)OsCl\({}_{4}\) doped with Eu\({}^{2+}\) were firstly reported by [PERSON] _et al._[13]. The luminescent properties of Sr\({}_{4}\)Si\({}_{3}\)OsCl\({}_{4}\) doped with Eu\({}^{2+}\) were optimized by co-doping with some divalent metal ions (such as Ca\({}^{2+}\), Mg\({}^{2+}\), Zn\({}^{2+}\)) [14, 15, 16].  
In this paper, Sr\({}_{4}\)Si\({}_{3}\)O\(8\)Cl\({}_{4}\) co-doped with Eu\({}^{2+}\), Bi\({}^{3+}\) were prepared by the high temperature reaction in the reduction atmosphere. The luminescent properties of Sr\({}_{4}\)Si\({}_{3}\)OsCl\({}_{4}\):Eu\({}^{2+}\), Bi\({}^{3+}\) were investigated. Finally, single blue-green LED was fabricated by combining the blue-green phosphor with \(\sim\)395 nm emitting LED chips.
## 2 Materials and Methods
### HPGe \(\gamma\)-Spectrometry Measurements  
HPGe \(\gamma\)-ray spectrometry was used to estimate the natural radioactivity contents of different commercially available pigments. In addition, in order to evaluate any possible radiological health risk for the public, calculations of the absorbed \(\gamma\)-dose rate (\(D\)), annual effective dose equivalent outdoor (\(AEDE_{out}\)) and indoor (\(AEDE_{in}\)), and activity concentration index (\(I\)) were performed [29].  
For the HPGe \(\gamma\)-spectrometry analysis, pigments were desiccated in order to completely remove moisture and to achieve constant mass in Marinelli hermetically closed boxes of 250 mL capacity [30].  
Additionally, samples were analyzed for 70,000 s in order to evaluate the activity concentration of \({}^{226}\)Ra, \({}^{232}\)Th, and \({}^{40}\)K, as reported in [31]. The minimum detectable activity (MDA) is of the order of 0.1 Bq kg\({}^{-1}\) dry weight, d.w., in all cases, according to [30].  
The Ortec HPGe detector operating parameters are reported in Table 2[31].  
The detector was placed inside lead sums in order to screen the background environmental radiation. Noteworthy, calibrations in terms of efficiency and energy were carried out via the use of a 250 mL capacity Marinelli multi-peak gamma source (BC-4464) [32]. The density correction in the efficiency curve for detection was directly carried out by using the Gamma Vision 8.0 (Ortec) software during data analysis [33].  
In order to assess the specific activity (Bq kg\({}^{-1}\) d.w.) of \({}^{226}\)Ra, \({}^{232}\)Th, and \({}^{40}\)K, the following formula was used [34]:  
\[C=\frac{N_{E}}{\epsilon_{E}\times t\times\gamma_{d}\times M} \tag{1}\]  
where \(N_{E}\) indicates the net area of a peak at energy \(E\); \(\epsilon_{E}\) and \(\gamma_{d}\) are the efficiency and yield of the photopeak at energy \(E\), respectively; \(M\) is the pigment mass (kg); and \(t\) is the counting time (s).  
\begin{table}
\begin{tabular}{c c} \hline \hline \multicolumn{2}{c}{**HPGe Detector**} \\ \hline Full Width at Half Maximum & 1.94 keV \\ Peak-to-Compton ratio & 65:1 \\ Relative efficiency & 37.5\% (at the 1.33 MeV \({}^{60}\)Co \(\gamma\)-line) \\ Bias voltage & \(-\)4800 V \\ Energy range & 5 keV\(-\)2 MeV \\ \hline \hline \end{tabular}
\end{table}
Table 2: The HPGe detector operating parameters [31].  
Figure 1: The investigated Raw Sienna (**a**), Burnt Sienna (**b**), Raw Umber (**c**), Burnt Umber (**d**), Yellow ochre (**e**), French Ochre (**f**), Green Earth from Verona (**g**), Terra Pozzuoli (**h**), Titanium Orange (**i**), and Titanium White (**j**) pigments.  
The measurement uncertainty was estimated as reported in [35] and the Italian Accreditation Body (ACCREDIA) confirmed the quality of the experimental results [36].
# Unsupervised Temporal Adaptation in Skeleton-Based Human Action Recognition
## 3 Method
### Optimization  
The proposed UDA network (illustrated in Figure 1) consists of three components: an encoder \(\mathcal{M}\), a projector \(\mathcal{P}\), and a classifier \(\mathcal{C}\). The network training is carried out by the interaction among (2), (3), and (5), which are alternatively optimized (Figure 2) according to the stages below:* **CPL \(\mathcal{M},\mathcal{P}\)) updating**. The CPL model is initially trained with supervised contrastive learning utilizing labeled source domain data \(\left(X_{s}^{(i)},Y_{s}^{(i)}\right)\). Parameters in \(\mathcal{M}\) and \(\mathcal{P}\) are updated by minimizing the loss function (2) as follows: \[\min_{\mathcal{M},\mathcal{P}}\sum_{(X_{s}^{(i)},Y_{s}^{(i)})\in B_{S}}L_{ \text{SupCon}}\left(X_{s}^{(i)},Y_{s}^{(i)};\mathcal{M},\mathcal{P}\right)\] (6)
* **TAM (\(\mathcal{C}\)) updating**. The classifier \(\mathcal{C}\) is updated in an iterative refinement scheme. 1. Data and domain labels \(\left(X_{s}^{(i)},Y_{s}^{(i)}\right)\) are used to update \(\mathcal{C}\) to improve semantic classification. It is achieved by minimizing the loss functions (3) as in (7), where \(\lambda\) is the trade-off weight used to balance the **TAM** training. At this step, \(\overline{\mathcal{M}}\) is fixed. 2. The pre-optimized \(\mathcal{C}\) is utilized to obtain the pseudo-label \(\hat{Y}_{t}^{(i)}\) of \(X_{t}^{(i)}\) based on Equations (4) and (5) as in Equation (8). Note that \(\overline{\mathcal{C}}\) and \(\overline{\mathcal{M}}\) are fixed at this step. 3. Afterwards, data and pseudo-labels \(\left(X_{t}^{(i)},\hat{Y}_{t}^{(i)}\right)\) are used to update \(\mathcal{C}\). \[\min_{\mathcal{C}}\left[\lambda\sum_{X_{t}^{(i)}\in B_{t}}L_{\text{CE-t}} \left(X_{t}^{(i)},\hat{Y}_{t}^{(i)};\overline{\mathcal{M}},\mathcal{C}\right)\right]\] (9) Steps (1), (2), and (3) are implemented in each iteration of the **TAM** stage.
# Performance Optimization of MANET Networks through Routing Protocol Analysis
## 2 Materials and Methods
### Test Design  
The simulation scenarios' design was used to compare the performance of variations of the AODV routing protocol and the default OLSR routing protocol. Here, we used a scenario that consisted of five nodes containing one moving node and four motionless nodes. The amount of network traffic occurred for only one connection with one source and one destination node, as shown in Table 2. This destination node would move away from the source node, resulting in changes in the routing path. It was used to test the changes of the parameters RREQ_RETRIES and MAX_RREQ_TIMEOUT on the AODV protocol. Figure 1(a),b shows the test scenario's start position and end position. These two images show that node 3 moved away from node 0 and approached node 4. There were four variations in node speed--5 m/s (18 km/h), 8 m/s (28.8 km/h), 11 m/s (39.6 km/h), and 20 m/s (72 km/h)--which show the condition of the vehicle speed at different crowd levels. There was also a condition where the connection between the source and destination nodes was disconnected in order to perform a more detailed analysis of the effect of the RREQ_RETRIES and MAX_RREQ_TIMEOUT parameters on the AODV protocol on the formation of new network paths.
## 2 Materials and Methods
### Calibration Curve of SIM-CAP Compound  
We used a CoM 6000 HPLC system (Albany, New York, NY, USA) with a Comatex C18-AB column (250 \(\times\) 4.6 mm, 5 um; 115 Corp, Plainfield, IL, USA) to measure the CAP and SIM concentrations in the samples. The wavelength was 230 nm. The mobile phase consisted of acetonitrile, water, and phosphoric acid (\(r/v/v\), 300:100:0.1) at a 1.0 mL/min flow rate at 35 \({}^{\circ}\)C. All mobile phases were degassed; the sample volume was maintained at 20 \(\mu\)L. The SIM-CAP complex stock solution was prepared with five concentration gradients of 50, 100, 200, 300, and 500 \(\mu\)g/mL. The peak area values corresponding to different concentrations were determined according to the chromatographic conditions mentioned earlier. Linear analysis was performed with concentration as the x-axis (X) and peak area values as the y-axis (Y) to obtain a standard curve.  
Figure 1 shows high-performance liquid chromatograms of simvastatin and captopril with injecting 200 \(\mu\)g/mL under the chromatographic conditions shown in Section 2.3. The retention times of CAP and SIM are 2.6 min and 11.8 min.  
Table 1 shows the peak areas corresponding to different concentrations of SIM-CAP compound. The standard curve was obtained with concentration as the x-axis (X) and peak area values as the y-axis (Y). The standard curve equation for SIM-CAP compound is Y = 45,307X \(-\) 1,695,273, with a correlation coefficient R\({}^{2}\) = 0.9956 (Figure 2). This indicates a good linear relationship between the concentration of SIM-CAP compound and peak area within the concentration range of 50-500 \(\mu\)g/mL.  
Figure 1: High-performance liquid chromatograms of simvastatin and captopril.
# Assessment of CYGNSS Wind Speed Retrievals in Tropical Cyclones
## 5 Wind Assessment in Tropical Cyclones
### L2 CYGNSS Wind Products
Panels **(c,f)** display the scatterplot and statistics for the colocated CYGNSS versus AMSR2 winds in the TC scenes for the NOAA V1.1 and V3.0 YSLF datasets, respectively. The displayed AMSR2 pass is at 1830 UTC. The bias sign refers to AMSR2 minus CYGNSS. The scatterplots (**c,f**) are color-coded based on different colocation time windows, from 1 to 4 h.  
above 30 m/s. Regardless of the choice of radiometer, the standard deviation is just above 3 m/s, and the correlation coefficient is above 0.75, confirming a reliable performance of the NOAA V1.1 in TCs. This suggests a need for the NOAA GMF calibration to be modified at high winds. The NOAA CYGNSS high wind performance will be revisited in the upcoming version, currently under development and planned for release in early 2022, where a modified version of ECMWF model winds, used in the track-wise sigma0 bias correction algorithm, will be used in conjunction with HWRF winds (Faozi Said, personal communication). This is expected to bring the NOAA CYGNSS high wind retrievals into much better agreement with the radiometers and with in situ measurements such as the Stepped Frequency Microwave Radiometers (SFMRs) onboard hurricane-penetrating aircrafts [52]. A quantitative statistical assessment of the SDR V3.0 and CDR V1.0 YSLF winds is shown in Figure 12 and confirms what was already inferred from the sample storm case studies: both datasets have significant biases (on average 6.8 and 4.8 m/s, respectively) and noise (standard deviations above 7 m/s and very poor correlation) compared to radiometer winds. For this reason, the CYGNSS YSLF datasets cannot be used to determine important storm features, such as intensity and size (radii) of storm/hurricane-force winds, with the reliability required for storm forecasts. Improved versions are currently being developed by the CYGNSS science team investigators, including new calibration corrections and new GMFs at high winds. The release of a new version of the SDR dataset is expected in the coming months, but it does not include a track-wise bias adjustment, and it is not expected to significantly improve the retrievals at high winds. Figure 10: Similar to Figure 9, but for Super Typhoon Hagibs on 11 October 2019. The displayed AMSR2 pass is at 0400 UTC.
# Evaluating Pile-Supported Embankment Considering the Soil Anisotropy Effect
## 5 Soil Arching Considering Friction Angle Anisotropy
### Friction Angle Parameter Analysis  
In order to study the influence of the anisotropy degree of friction angle on the stress distribution and settlement in embankment, the anisotropy degree of friction angle is defined by setting different anisotropy coefficients for the condition that the friction angle \(\varphi\) of cohesionless soil measured by triaxial test is \(30^{\circ}\). According to the measured values of \(\varphi_{v}\) and \(\varphi_{h}\) in Reference [52], it can be seen that the anisotropy coefficient (\(A_{r}\)) of the friction angle is less than 1.5. Therefore, this paper only studies the influence of the anisotropy of friction angle on embankment structure in this range. Figure 14 is the variation curve of the friction angle with the direction of shear stress \(\theta\) when the anisotropy coefficients of different friction angles are calculated according to Equation (29). When the shear stress direction is vertical, the shear strength of the soil is fully exerted, and the friction angle takes the maximum value \(\varphi_{v}\); when the shear stress direction is horizontal, the shear strength of the soil is the smallest, and the friction angle takes the minimum value \(\varphi_{h}\). When the value of \(\theta\) is 45-\(\varphi_{v}/2\), the friction angle is \(30^{\circ}\). The friction angle anisotropy research model plan is shown in Table 4. In the finite element analysis, the cohesion cannot be set to zero, so it is taken as 0.1 kPa.  
In the UMAT program, the friction angle is stored by the fifteenth state variable (SDV15). Figure 15 shows the distribution of the friction angle anisotropy coefficient when the model calculation is completed. Consistent with the anisotropic distribution of cohesion, the pile top position and the area outside the influence range of soil arching are not affected. The friction angle near the center of the two piles decreases, but the minimum value is not at the center of the two piles, indicating that the shear stress at the center of the two piles is not horizontal.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{1}{c}{**Anisotropic Coefficient of**} & \multicolumn{1}{c}{**Force of Cohesion**} & \multicolumn{2}{c}{**Friction Angle (\({}^{\circ}\))**} \\ \cline{2-4}
**Friction Angle (\(A_{r}\))** & **(kPa)** & \(\varphi_{v}\) & \(\varphi_{h}\) \\ \hline
1 & 0.1 & 30 & 30 \\
1.09 & 0.1 & 30.5 & 28.4 \\
1.19 & 0.1 & 31 & 26.8 \\
1.32 & 0.1 & 31.5 & 24.9 \\
1.48 & 0.1 & 32 & 22.9 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Anisotropy of the friction angle research model plan.  
Figure 14: Variation of the friction angle in terms of \(\theta\) for various values of (\(A_{r}\)).
# Design and Implementation of a RFID Reader/Router in RFID-WSN Hybrid System
## 5 Communication Systems of Integrated ZigBee and RFID Technology  
### Communication System Hardware  
Hardware of communication systems of integrated ZigBee and RFID technology includes ZigBee coordinator, RFID reader/router, ZigBee sensor node, and RFID tags, as shown in Figure 11.  
Because of the low power consumption of the ZigBee technology, all modules in Figure 11 are powered by dry battery. The ZigBee coordinator transmits data with host computer by USB. RFID reader/routers are responsible for collection of information and forwarding instruction of RFID reader. ZigBee sensor nodes are responsible for controlling the RF module to read, write and modify the relevant data in electronic tag.