esa-sceva/satcom-chunk-collection huggingface dataset:
# Augmented MIMO: Body-Mounted Antennas for Tiny Wearable Devices
## 5 Results  
### Line-of-Sight Environment  
The results of the throughput measurement in the LOS environment with the IPerf TCP mode are shown in Figure 9. The measured throughput was, at most, about 110 Mb/s, which is significantly lower than the throughput of the gigabit Ethernet between the WLAN router and the server PC, and of the USB 3.1 interface between the WLAN dongle and the client PC. Therefore, the measured throughput can be considered as a representation of the throughput of the WLAN air interface.  
For the chip-to-AP link and the Augmented MIMO link, three trials each were performed in alternating order to verify the reproducibility of the measurement. In each trial, the throughput was measured 100 times at 1 s intervals. The 100 s measurement time was determined based on the periodic throughput fluctuations observed in the preliminary measurements. The fluctuation period was about 30 s, as can be seen in Figure 9, so the measurement time was set to be sufficiently longer than the fluctuation period.  
Figure 8: Throughput measurement in an NLOS environment with many scattered objects, such as stools placed on desks. The WLAN router is placed behind the whiteboard and is 8.8 m distant from the client.  
Figure 9: The throughput evaluation results, including three trials each for the chip-to-AP link (plotted with “\(\times\)” markers) and Augmented MIMO link (“\(\circ\)” markers). Different colored lines indicate different trials. Augmented MIMO was, on average, more than twice as fast as the chip-to-AP link.  
The measured throughput of the chip-to-AP link was 32.4 Mb/s, 33.8 Mb/s, and 31.8 Mb/s on average for each trial. On the other hand, three trials with Augmented MIMO achieved average throughputs of 65.1 Mb/s, 65.7 Mb/s, and 79.7 Mb/s. Thus, Augmented MIMO increased the throughput by a factor of 2 or more on average.
## 3 Results and Discussion
### Antioxidant Activity, Total Phenols  
To demonstrate whether and to what extent browning development affects the antioxidant status of white wines, antioxidant activity (AR) was measured during wine heating for twelve days (Figure 3a,b). AR showed a slight increase during heating at 35 \({}^{\circ}\)C, while at 50 \({}^{\circ}\)C, the opposite was observed. The existing data on the evolution of wine antioxidant activity with time is rather conflicting. Several studies [1; 43; 44] concluded that AR decreases or remains stable with biological or accelerated ageing, whereas others [26; 33; 42] stated that heat-treated or aged wines have higher ability for free radical scavenging than the non-aged do. These conflicting results reflect the different behavior of wines made by different varieties or winemaking technologies and the inconsistency induced by the different methods employed for AR determination.  
The decrease in the free form of SO\({}_{2}\) content which was more pronounced at 50 \({}^{\circ}\)C (Figure 1) might be responsible for this different trend observed in AR values of the wines heated at two different temperatures.  
In addition, after 12 days of heating at both temperatures, the AR of the samples did not show any significant differences. One exception was the antioxidant activity of the control sample at 35 \({}^{\circ}\)C, which was significantly higher than the respective values of the rest of the samples. Although the chemical composition of the samples studied in this experiment might differ after the heating period, the similar AR values observed might be attributed to the different condensation products which were formed and which display antioxidant activity sometimes even higher than that of the original monomeric compounds [26; 45].  
Figure 2: Total sulfhydryl groups (expressed as mg GSH/L) of samples after 12 days of heating at 35 (**a**) and 50 \({}^{\circ}\)C (**b**). Different letters at each temperature studied indicate statistically significant differences (_p_ < 0.05). Sample codes are described in Table 1.  
Figure 3: Change of antioxidant activity (TE mg/L) **(a,b)** and total phenols **(c,d)** GAE (mg/L) of the samples during 12 days of heating at 35 and 50 \({}^{\circ}\)C, respectively. Different letters at each temperature studied indicate statistically significant differences (\(p<\) 0.05). Sample codes are described in Table 1.  
These results are in agreement with the data obtained concerning total phenols measured by the Folin-Ciocalteau method (Figure 3c,d). No significant differences were observed between the samples after the 12 days of heating at both temperatures. This spectrophotometric method is based on the oxidation of the phenolic hydroxyl groups with a simultaneous reduction of the Folin-Ciocalteau reagent [46]. The absence of differences in total phenolic content among the samples may possibly be attributed to the transformation of phenols into more condensed forms that possessed similar chemical properties and reactivity towards the reagent.
# A Secure IoT-Based Authentication System in Cloud Computing Environment
## 3 Proposed Scheme
### Password Change Phase  
If the user \(U_{i}\) needs to change the password, you may need to start the password change phase. First, we assume that the smart card of \(U_{i}\) contains ( \(C_{1}^{\prime}\), \(C_{2}\), \(C_{3}\), \(C_{4}\), \(PID_{i}^{\prime}\), \(ID_{cs}\)). The \(U_{i}\) inserts the smart card into the card reader for key verification in identity \(ID_{i}\) and the original password \(PW_{i}\). The smart card will calculate \(n_{i}=C_{3}\oplus h(ID_{i}||PW_{i})\) and check \(h(ID_{i}||PW_{i}||u_{i})?=C_{4}\). If the equation holds, \(U_{i}\) can input the new password \(PW_{i}^{\prime}\). The smart card calculates \(C_{2}^{\prime}=C_{2}\oplus h(PW_{i}||n_{i})\oplus h(PW_{i}^{\prime}||n_{i})\), \(C_{3}^{\prime}=C_{3}\oplus h(ID_{i}||PW_{i})\oplus h(ID_{i}||PW_{i}^{\prime})\), and \(C_{4}^{\prime}=C_{4}\oplus h(ID_{i}||PW_{i}||n_{i})\oplus h(ID_{i}||PW_{i}^{ \prime}||n_{i})\) and replaces (\(C_{2}\), \(C_{3}\), \(C_{4}\)) with (\(C_{2}^{\prime}\), \(C_{3}^{\prime}\), \(C_{4}^{\prime}\) ). Finally, there are (\(C_{1}^{\prime}\), \(C_{2}^{\prime}\), \(C_{3}^{\prime}\), \(C_{4}^{\prime}\), \(PID_{i}^{\prime}\), \(ID_{cs}\)) in the smart card, and \(U_{i}\) can use the new password \(PW_{i}^{\prime}\) to perform the authentication phase in the next round. The flowchart of password modification phase is shown in Figure 4.  
Figure 4: The flowchart of the password change phase.
## 3 Suichang Gold Mine Shrinkage Method to Filling Method Case
### Upward Horizontal Layered Filling Method  
The upward horizontal layered filling method is one of the most widely used backfilling mining methods at home and abroad, which is characterized by the following: the ore block is divided into rooms and pillars, and the ore rooms are mined first, and then the pillars are mined. Both the upward horizontal layered filling method and the shrinkage method use a bottom-up layered method to recover ore, but there are significant differences between them. The shrinkage method uses the recovered ore to maintain the stability of the surrounding rock in the hanging wall and foot wall and create the conditions for further ore recovery. In contrast, the upward horizontal layered filling method uses a backfill formed by tailings and cementitious materials to maintain the stability of the surrounding rock in the hanging wall and foot wall and to create conditions to support personnel and equipment to continue recovering ore. In addition, after the stope operation is completed, the backfill remains in the goat to provide support for the goat, ensure the safety of the stope, and effectively prevent surface subsidence. However, in the shrinkage method, as the ore is fully recovered, the goat loses its support, which may eventually lead to goat collapse and surface subsidence. The three views of the backfilling method are shown in Figure 8.  
Figure 8: Mechanized upward horizontal layered filling method. I-I is the front view of the mining method, II-II is the left view of the mining method, and III-III is the top view of the mining method.  
Figure 7: Geographic location of the mine (**left**), tailings pond (**right**).  
In the practical application of upward horizontal layered filling in Suichang Gold Mine, the dimensional parameters of the mine house are determined by an engineering analogy. Specifically, the length and height of the mining room were set at 150 m and 30 m, respectively, while the height of the layers was set at 3.3 m. When determining the size of the pillar, it is necessary to consider both the loss of ore and the safety and security. Larger pillars, while providing better security, can lead to more ore loss; smaller pillars, while able to recover more ore, can pose a safety hazard. In view of the high ore grade of the Suichang Gold Mine and to ensure the safety and stability of the stope, the decision was made to use a 4-metre-wide rib pillar.
## 4 Discussion
### Crust Thickness Measurements
#### 4.3.1 Effect of Biopolymer Type  
_Medium-grained sand._ The crust thickness measurements showed that all tested biopolymers produced crusts, with the biopolymer type significantly affecting the resulting crust thickness (Figure 9 and Table 5). These findings correspond with the published literature (Table A9). In contrast to the other biopolymers, NLS, HEA, HG, and TG partially formed very thin and brittle crusts (Figures 10 and 11). Regarding NLS, this can be explained as previous studies found that lignosulfonates must be applied at concentrations >2 wt% to enhance soil mechanical properties effectively [19; 33; 68]. The ability of HEA and HG to enhance soil mechanical properties has not yet been studied, but their treatments at1 wt% and 2 wt% (only HEA) did not increase inter-particle cohesion sufficiently to form a competent crust.  
_Fine-grained silica sand_. The test results showed that the biopolymer type significantly affects the crust thickness (Figure 12 and Table 5), which agrees with the existing literature (Table A9). Compared to the other tested biopolymers, NLS, POS, HEA, and PP formed relatively thick crusts, likely because of a favourable combination of viscosity and bonding strength, allowing for a high infiltration depth and sufficient inter-particle cohesion to form a thick crust. By contrast, WS and XG only formed thin crusts because of their high viscosity. Hence, on fine-grained silica sand, biopolymer viscosity is a limiting factor regarding the achievable crust thickness. Treatments on fine-grained silica sand generally formed thicker crusts than on medium-grained sand, as the finer particle size and tighter packing of the silica sand favour particle agglomeration and crust formation. In this context, NLS, HG, and HEA exhibited significant differences, forming only thin and brittle crusts on medium-grained sand but achieving considerably higher crust thicknesses on fine-grained silica sand.
## 3 Statistical Criteria for Evaluation of Models  
To check the precision of the proposed models, different statistical criteria including \(R\), root mean square error (RMSE), mean absolute percentage error (MAPE), Scatter Index (SI), and Willmott's agreement Index (IA) were employed [53; 54; 55; 56; 57; 58; 59; 60; 61; 62; 63; 64; 65; 66].  
1. Correlation coefficient (R) can be expressed as  
\[\mathrm{R}=\frac{\sum_{i=1}^{N}\left(\mathrm{Bl}_{\mathrm{p,i}}-\overline{ \mathrm{Bl}}_{\mathrm{p}}\right).\left(\mathrm{Bl}_{\mathrm{o,i}}-\overline{ \mathrm{Bl}}_{\mathrm{o}}\right)}{\sqrt{\sum_{i=1}^{N}\left(\mathrm{Bl}_{ \mathrm{p,i}}-\overline{\mathrm{Bl}}_{\mathrm{p}}\right)^{2}\sum_{i=1}^{N} \left(\mathrm{Bl}_{\mathrm{o,i}}-\overline{\mathrm{Bl}}_{\mathrm{o}}\right)^{ 2}}},\ 0<\mathrm{R}<1 \tag{14}\]  
Figure 4: Training procedure of bagged regression tree (BRT).  
2. Root mean square error (RMSE) can be expressed as \[\text{RMSE}=\left(\frac{1}{\text{N}}\sum_{i=1}^{\text{N}}\left(\text{BI}_{\text{ o,i}}-\text{BI}_{\text{p,i}}\right)^{2}\right)^{0.5}\] (15)
3. Mean absolute percentage error is defined as \[\text{MAPE}=\frac{100}{\text{N}}\sum_{i=1}^{\text{N}}\frac{\left|\text{BL}_{ \text{o,i}}-\text{BI}_{\text{p,i}}\right|}{\text{BI}_{\text{o,i}}}\] (16)
4. Scatter Index can be expressed as \[\text{SI}=\text{RMSE}/\overline{\text{BI}}_{\text{o}}\] (17)
5. Willmott's agreement Index [49] can be expressed as \[\text{I}_{\text{A}}=\frac{\sum_{i=1}^{\text{N}}\left(\text{BI}_{\text{o,i}}- \text{BI}_{\text{p,i}}\right)^{2}}{\sum_{i=1}^{\text{N}}\left(\left|\text{BI}_ {\text{o,i}}-\overline{\text{BI}}_{\text{o}}\right|+\left|\text{BI}_{\text{o,i}}-\overline{\text{BI}}_{\text{o}}\right|\right)^{2}},\ 0<\text{I}_{\text{A}}<1\] (18)  
where \(\text{BI}_{\text{o}}\) is observed value; \(\text{BI}_{\text{p}}\) is predicted value; \(\overline{\text{BI}}_{\text{o}}\) and \(\overline{\text{BI}}_{\text{p}}\) are average values of observed and predicted data, respectively; and N is the number of data.
# A Survey on Malleability Solutions for High-Performance Distributed Computing
## 3 Fault Tolerance for Malleability  
Fault tolerance and malleability are important issues in high-performance computing. Fault tolerance is especially relevant in large-scale systems, where the likelihood of node failures is extremely high. The term malleability captures applications' ability to incorporate, or release, resources upon system request. This issue becomes crucial in the performance of applications and systems, as it implies a significant overhead.  
Both approaches share similar features, because they need to stop an application execution and restart it from the last valid checkpoint. Although from a malleability standpoint, there are two main differences:  
1. Malleable applications have to be restarted each time they are resized and checkpoint phases are only triggered when such reconfiguration is required.
2. Reconfigurations may consist of allocating more or few resources. In any case, it is very important to address the data redistribution in the new process layout.  
Furthermore, several techniques used in fault tolerance are leveraged in malleability. One of the main ones is the checkpoint and restart technique (C/R). This strategy consists of periodically writing an application state to reliable storage, typically a parallel file system. Upon failure, an application can restart from a prior state by reading its checkpoint file.  
In the following, some of the most important efforts to adopt malleability in C/R techniques are described.
## 2 O-RAN Architecture and Development  
O-RAN architecture is a virtualized, software-driven, and open radio access network (RAN) architecture that enables the integration of hardware and software components from multiple vendors. It is designed to be modular, scalable, and flexible, with standardized interfaces that enable interoperability between different RAN components. The O-RAN architecture consists of multiple functional components that can be separated and managed independently. Some of the key components in O-RAN are Radio Unit (RU), Distributed Unit (DU), Central Unit (CU), RAN Intelligent Controller (RIC), and Service Management and Orchestration (SMO). In the following, we shall first describe the O-RAN alliance, architecture and softwarization developments, as well as their role in ML for O-RAN network automation.
## 2 Principle and Experiment
After this, serial-to-parallel (S/P) conversion is performed to retrieve the information in subcarriers. Zero-forcing scheme is used to restore the signal after the channel, and fast Fourier transform (FFT) is used to convert it into the frequency domain. In the PowDM decoding, Data 2 can be decoded first while considering Data 1 as noise, so direct QPSK demodulation can be applied. Then, SIC is employed to subtract the retrieved  
Figure 2 illustrates the experiment of the proposed PowDM VLC system. A photo of the experimental setup is shown in inset of Figure 2. As discussed in previous paragraphs, Data 1 and Data 2 with proper power ratio are combined in the digital domain using off-line program. Then, the PowDM data stored in an arbitrary waveform generator (AWG, Tektronix(r) AWG 70001) act as the DAC converting the PowDM digital data into real electrical waveform to drive a LD. The AWG has the sampling rate of 50 GSample/s and resolution of 8 bits. It has an analog frequency range of 18 GHz. Then, the PowDM electrical signal is used to drive a red LD via a bias-tee circuit. The red LD (Opnext(r) HL6544 FM) used has a peak wavelength of 640 nm. It is based on AlGaInP with multi-quantum well (MQW) structure. After a free-space transmission distance of 1.5 m, the optical signal is detected by a PIN PD (EOT ET-2030A). After the optical signal has propagated 1.5 m transmission distance, the optical beam diameter at the Rx is 4 mm. It is silicon-based with a bandwidth of 1.2 GHz. The active area diameter and the acceptance full angle are 400 \(\upmu\)m and 20\({}^{\circ}\), respectively. The PD is attached to a real-time oscilloscope (RTO, Teledyne LeCroy(r) 816 ZI-B) acting as the ADC converting the received waveform into digital data for the 1.2 GHz. The output of the ADC is then fed to the ADC by a PIN-PD (EOT ET-2030A). The output of the ADC is fed to the ADC by a PIN-PD (EOT ET-2030A). signal processing. The RTO has the sampling rate of 80 GSample/s and analog bandwidth of 16 GHz. In the experiment, the FFT size 512, number of subcarriers is 125, and CP is 32. Table 1 summarizes all the system parameters.
# Applying Wearable Technology and a Deep Learning Model to Predict Occupational Physical Activities
## 2 Materials and Methods
### Occupational Physical Activities and Manual Material Handling Tasks  
In this study, 15 categories of occupational physical activities were selected with some OPAs performed in multiple ways to capture the variation of physical activities and prevent overfitting of the model (Table 1). To train the models, subjects performed activities with fixed parameters of load, duration, and repetition. Most OPAs were performed for at least 60 s each (Table 1). Figure 1: Study procedure of prediction of occupational physical activities using inertial measuring units and deep learning model. A subset of participants (\(n=9\)) completed up to three simulated work tasks including bottle packing, carpet laying, and drilling to test the deep learning model's ability to predict OPAs while performing simulated work tasks (Figure 2). Except for sitting which exhibited obvious features for prediction, the bottle packing, carpet laying and drilling tasks included all OPAs (Table 2). Verbal explanation and visual demonstration were provided for subjects prior to performing the various OPAs and simulated work tasks. The whole procedure took approximately 2 h per subject. \begin{table}
\begin{tabular}{c c c c c c} \hline
**MMH Task** & **N** & **Duration/Repetition** & **OPA Category Covered** \\ \hline Bottle packing & 6 & Within 5 min & Standing, reaching, lifting/lowering, walking and carrying \\ \hline \multirow{3}{*}{Carpet laying} & \multirow{3}{*}{4} & \multirow{3}{*}{Within 5 min} & Walking, lifting/lowering, one-handed lifting/lowering, pushing, pulling, one-handed pulling, crouching, stooping, crawling, kneeling and standing \\ \hline \multirow{3}{*}{Drilling} & \multirow{3}{*}{4} & \multirow{3}{*}{15 s} & Walking, lifting/lowering, carrying, stooping, overhead work and standing \\ \cline{1-1} \cline{3-5}  & & & & \\ \hline \end{tabular}
\end{table} 
Table 2: Manual material handling tasks (\(n=9\)). Subjects complete MMH tasks with different handling height and OPAs covered by each task are listed.
# Towards Flexible Distribution Systems: Future Adaptive Management Schemes
## 3 Future Adaptive Control and Management Methods
### Adaptive DER Unit QU-Droops: Stage 2-3  
In order to achieve better voltage support and maximize PV and EV hosting capacity in distribution networks as well as improve the availability of distribution network-connected DER for provision of TSO flexibility services, the DER unit _QU_-droop settings could be real-time adaptive in the future at evolution stage 2 and 3 (Table 1) instead of time-based adaptation as presented in Table 3 and [81]. If the real-time OLTC setting value could be communicated to the DER unit, then it could be used as an input to adapt the DER unit _QU_-droop settings, as shown in Figure 4. Regarding the other _QU_-droop settings (Figure 4), the constants could be chosen so that the location-based prioritization principles defined in Table 4 are also fulfilled. In case of communication failure, the seasonal/monthly, etc., time-based DER unit _QU_-droop settings adaptation could be used as a backup method.  
In addition, with DER units also participating in the control and management of _PQ_-flow between voltage levels or at the DER, household, or microgrid connection point (Table 5) by its reactive power control, the real-time adaptive _QU_-droop could be further modified from Figure 4, as shown in Figure 5.  
Figure 4: Real-time adaptive DER unit _QU_-droop-based on OLTC set value, OLTC setting value, location, and participation to rapid voltage control with (**a**) constants A and B [81] or (**b**) constant k (see Tables 3 and 4).  
As a further development from Figures 4 and 5, the _QU_-drop settings could in stage 3 be further enhanced with real-time measurements-based learning, i.e., based on previous local \(Q\) and \(U\) as well as connection point \(PQ\) flow measurements and use of AI and ML techniques. Regarding the measurements-based learning, it could be only enabled when frequency stays between, e.g., 49.95-50.05, in order to avoid confusion due to potential nearby DER units' \(Pf\)-control effects on local voltages.