esa-sceva/satcom-chunk-collection huggingface dataset:
# FBG-Based Monitoring of Geohazards: Current Status and Trends
## 3 Development of FBG-Based Geohazard Monitoring Systems
### Development of FBG Sensors for Other Applications  
In addition to temperature, strain, displacement and pressure, FBG sensors can also be applied to measure other physical parameters such as humidity and moisture. The reviews by [PERSON] et al. [57] and [PERSON] et al. [58] detailed the recent development of this kind of sensor, and concluded that such concept is still fairly new. While interesting works have been reported on the use of FBG sensors for soil moisture measurement [59; 60], its applications in geohazard monitoring have still been rare. It is noteworthy that, using ROTDR or BOTDA technologies, distributed temperature sensing (DTS) optical fiber cables have been successfully applied to monitor soil moisture or seepage [61; 62; 63].  
Another potential of FBG sensors is to measure ground vibrations generated by earthquakes, landslides, debris flows, rock falls, sinkhole collapse, etc. An important parameter to consider when devising FBG accelerometers is the operating frequency range, which varies with different situations. [PERSON] et al. developed a fiber-optical sensing system comprising commercial FBG accelerators (operating frequency range: 0-300 Hz) for debris flow monitoring [64]. Field monitoring data were compared with conventional geophones, showing that this system was feasible for debris flow detection. The seismic waves induced by earthquakes have a much broader range, from \(10^{-4}\) Hz up to \(10^{2}\) Hz. Achieving such broad range for accelerometers, seismometer networks, or FBG-based sensors is still a tough job. An FBG seismic accelerometer was developed by [PERSON] et al. using a flexural cylinder beam [65]. This accelerometer can detect signals of a maximum frequency of 30 Hz, a maximum acceleration of 2.5 \(g\), and a dynamics of 100 dB. Such parameters were declared to meet most requirements for a seismic sensor.
## 2 Results and Discussion
### Extraction Time  
Extraction is the progressing of analytes achieving distribution equilibrium between the sample matrix, headspace, and the coating of the fiber, and a sufficient extraction time was used to achieve the equilibrium [33]. The coverage and content of captured volatile compounds could be affected by the extraction time [34]. Therefore, extraction time of 30, 40, and 50 min were studied for samples incubated at 80 \({}^{\circ}\)C for 30 min; the extraction temperature persisted at the incubation temperature. The result showed that 1.36 \(\times\) 10\({}^{8}\), 1.60 \(\times\) 10\({}^{8}\), and 1.71 \(\times\) 10\({}^{8}\) total chromatographic peak areas were obtained by extraction times of 30 min, 40 min, and 50 min, respectively. The number of identified volatile compounds were similar (58, 56, 56), but more compounds with higher molecular weight (>200) were detected under the extraction times of 40 min and 50 min (nine for 30 min, 19 for 40 and 50 min) and the chromatographic area proportion of these compounds had a substantial increase from 17.63% (30 min) to 25.64% (40 min) and 35.68% (50 min). Most were products of the thermal oxidation of fatty acids and fatty acid derivatives, which are odorless compounds or have slight odor with very high odor threshold value, with few effects on the aroma flavor of samples, but the high abundance of them probably prevents the adsorption of target analytes [35]. Consequently, the 30 min extraction condition was employed since it provided a superior balance between extraction efficiency and selectivity of analytes.  
Overall, the optimum HS-SPME GC-MS experimental conditions for sweet potato volatile compounds analysis were fixed as: the DVB/CAR/PDMS coating fiber, incubation at 80 \({}^{\circ}\)C for 30 min, and extraction at 80 \({}^{\circ}\)C for 30 min.
## 4 Modeling of the System Components
### Oil Pump  
Since the flow rate of the diathermic oil in the evaporator was not measured during the experiments, evaluation of its values was performed using the empirical models adapted from the literature. Regarding the gear pump in the hot circuit, the volumetric efficiency depends on the pump speed and the viscosity of the oil, which in turn depends on the working temperature of the oil. An empirical model of the gear pump volumetric efficiency suggested by [PERSON] et al. [18] was adopted here. This model was obtained from experimental tests of 16 external flow gear pumps. The volumetric efficiency is correlated to the Stribeck non-dimensional number, also known as non-dimensional viscosity, which was first introduced in 1901 [19] to represent the bearing friction curves and is defined as the following:  
\[\overline{\mu}=\frac{\mu\cdot\omega}{\Delta P}=\frac{2\pi}{60}\times\frac{\mu \cdot N}{\Delta P} \tag{10}\]  
where \(\mu\) is the fluid viscosity (Pa\(\cdot\)s), \(\omega\) the shaft speed (rad\(/\)s), and \(\Delta P\) is the pressure difference (Pa). The volumetric efficiency was calculated using the suggested correlation:  
\[\eta_{v}=1-\frac{6.8\times 10^{-9}}{\overline{\mu}}-\frac{2\times 10^{-5}}{ \sqrt{\overline{\mu}}} \tag{11}\]  
The volumetric efficiency of the gear pump is shown in Figure 11 at a given pressure and pump speed and varying the working temperature from 20 to 150 \({}^{\circ}\)C.  
Looking at Figure 11 and considering the operating temperature of the oil pump in the range of 120 to 150 \({}^{\circ}\)C,  
To calculate the pressure drop in the pipes and fittings of the diathermic oil circuit, the well-known Darcy-Weisbach friction factor was estimated using the implicit correlation for turbulent liquid flows in conduits suggested by [PERSON] and [PERSON] [20]:  
\[\frac{1}{\sqrt{f}}=-2\ln\left(\frac{\varepsilon/D}{3.7}+\frac{2.51}{Re\sqrt{f}}\right) \tag{12}\]  
The pressure drop in the pipes and fittings (fitting were considered using equivalent pipe length of each fitting) was calculated using Equation (13):  
\[\Delta P_{pipe}=\frac{1}{2}f\cdotp\cdotp v^{2}\frac{L}{D} \tag{13}\]  
Instead, the pressure drop correlation deduced for mineral oil flow in a PHE presented by [PERSON] [21] was used to calculate the Fanning friction factor of the hot fluid flow in the evaporator. This correlation is presented in Equation (14), and it was used as Equation (15) to calculate the pressure drop in the evaporator.  
\[f=\frac{11.215}{Re^{0.248}} \tag{14}\]  
\[\Delta P_{ev}=2f\cdotp\cdotp v^{2}\frac{L}{D} \tag{15}\]  
Therefore, the pressure drop of the hot fluid was calculated as the sum of Equations (13) and (15) to calculate the non-dimensional viscosity for all experimental data points. The resultant volumetric efficiency of the oil pump for the experimental database is shown in Figure 12 confirming the reliable performance of the oil pump in the temperature range of the tests.
# An Improved Hybrid Routing Protocol Combining MANET and DTN
## 2 Related Works
In order to limit the flooding-based copying, the number of neighbors to which the packet is forwarded is limited as a predefined cap. In Reference [20], a new algorithm for secure relay node selection in hybrid MANET and DTN based on the cooperation between routing, trust, and game theory was proposed. In the proposed algorithm, routing and data traffic is monitored in the networks and then the trust level for all mobile nodes are calculated. Then, trust is calculated and non-cooperative game theory is applied for relay node selection and the winning node is selected for relay node. Finally, data is sent to relay node and DTN forwarding mechanism is operated. In Reference [21], a hybrid MANET and DTN scheme was proposed to use stable MANET contacts and a utility based DTN routing protocol calculates a utility score when two nodes contact each other. In the proposed scheme, stable connections have better utility score. For each contact check interval, service discovery request is started and for all new contacts utility sores are calculated. Then, message forwarding is carried out based on updated utility score.
# Exploring Pre-Trained Models for Skin Cancer Classification
## 3 Materials and Methods
### XAI Techniques
#### 3.5.1 Integrated Gradients (IG)  
Integrated gradients is an attribution method designed to quantify the contribution of each input feature to the model's prediction. It calculates the average gradient of the model's output as the input transitions along a linear path from a baseline reference. The baseline, often set to zero or a neutral image, serves as a starting point for measuring feature importance. The attribution score for each feature is computed using the following formulation:  
\[IG_{i}(x)=(x_{i}-\hat{x_{i}})\times\int_{\alpha=0}^{1}\frac{\partial F(\hat{x} +\alpha(x-\hat{x}))}{\partial x_{i}}d\alpha \tag{5}\]  
where  
* \(IG_{i}(x)\) represents the attribution score for feature;
* \(x\) is the input instance;
* \(\hat{x}\) is the baseline input;
* F(x) is the model's output function;
* \(\alpha\) is a scaling factor interpolating between the baseline and the input.  
Integrated gradients satisfies two key properties:
* Sensitivity: If a feature significantly affects the model's prediction, it should receive a non-zero attribution score.
* Implementation Invariance: The attributions remain consistent across functionally equivalent neural networks.
## 2 Materials and Methods
### Cookies and Edible Film Preparation  
Three types of cookies were prepared according to [PERSON] et al. [20]: control cocoa cookies without fruit pomace in the recipe (CC), cookies enriched with grape and aronia pomace (GAP), and cookies with grape and aronia pomace covered with edible film (GAP with KGAE). The recipe for CC contained (with a percentage of spelt flour and oat flakes combined weight in brackets): fine rolled oats 300 g, whole spelt flour 252 g, margarine 200 g (36.2%), brown sugar 135 g (24.5%), cocoa powder 48 g (8.7%), vanilla sugar 35 g (6.3%), tap water 20 g (3.6%), salt 4 g (0.7%), baking powder 3 g (0.5%), and sodium bicarbonate 3 g (0.5%). In GAP and GAP with KGAE, 11 g (23.6%) of cocoa powder was replaced with a mixture of grape pomace (8.2 g which is 17.5% of cocoa) and aronia pomace (2.8 g which is 6.1% of cocoa) [18].  
The FFS, consisting of chitosan, GA and GSE, was prepared as described previously by [PERSON] et al. [21]. First, chitosan powder was dissolved in 1% (\(v/v\)) aqueous acetic solution while GA powder was dissolved in distilled water to obtain 5% (\(w/v\)) solutions. Then, GSE (1 g/L) was added to the GA solution and mixed for 40 min on a magnetic stirrer. Finally, the GA containing GSE and the chitosan solutions were mixed in a 50:50 ratio to obtain an active FFS (KGAE). After spraying KGAE over the GAP cookies, they were dried for 30 min at 80 \({}^{\circ}\)C in an oven [20].  
CC, GAP, and GAP with KGAE were used for nutritional and biochemical analyses as well as for the determination of starch digestibility, while GAP with KGAE was used in the intervention study along with commercial cookies.  
Commercial cookies (Cioccoograno, Mulino Bianco, Barilla) purchased at the local market contained 22 g of fat of which 10 g were saturated fatty acids, 60.4 g of carbohydrates of which 23.4 g were sugars, 7.6 g of protein, 6.3 g of dietary fiber and 0.43 g of salt per 100 g.
## Applications
#### 3.1.2 Vertical Federated Learning (VFL)  
Vertical Federated Learning, also known as feature-based FL, involves datasets that have different feature spaces but share the same sample ID space [41; 41]. This scenario occurs when different organizations or devices hold complementary information about the same set of entities [42]. For example, one organization may have demographic information about individuals, while another has purchasing behavior data [43]. By combining these features, a more comprehensive model can be trained without sharing raw data.  
**Applications**  
* Marketing: Combining customer demographic data with purchasing data to improve recommendation systems.
* Financial Services: Integrating credit scores from one institution with transaction histories from another to enhance risk assessment models.
## 3 Results
### R\({}_{\rm exo}\) vs. GPP Plot for Howland Forest CO\({}_{2}\) NEE (US-Ho1,2,3)  
Figure 3 shows yearly R\({}_{\rm exo}\) vs. GPP for the three Howland towers (US-Ho1,2 blue points; US-Ho3 orange points) and regression lines (using all data, solid red line; using only US-Ho3 data, solid orange line). The identity (dashed black line) and FLUXNET regression (dashed green line) lines were added as a reference. (A) Shows all the available years, while (B) shows only those in the ranges (1050; 1400) and (1250; 1600) gC m\({}^{-2}\) y\({}^{-1}\) for R\({}_{\rm exo}\) and GPP, respectively. Individual years are identified in B); years with higher GPP relative to R\({}_{\rm exo}\) likely store more carbon relative to annual records for carbon sequestration shown; larger GPP is offset by larger R\({}_{\rm exo}\). Outliers in 3-A for US-Ho1 and US-Ho2 (filled blue circles) from lowest to highest GPP are US-Ho1 (2015), US-Ho1 (2009), US-Ho3 (2008), and US-Ho1 (2001).  
Annual Budgets of CO\({}_{2}\), CH\({}_{4}\) and N\({}_{2}\)O for Howland Tower (US-Ho1) and Soil Accumulation Chambers  
Figure 4 shows yearly budgets (bars) and trends (lines) for CO\({}_{2}\) (A) and CH\({}_{4}\) (B) fluxes measured at the top of the tower, and soil CO\({}_{2}\) (C), CH\({}_{4}\) (D) and N\({}_{2}\)O (E) fluxes measured at chambers no. 2 (CO\({}_{2}\) and CH\({}_{4}\)) and no. 10 (N\({}_{2}\)O). Soil fluxes budgets from (C,D and E) correspond only to May-November, while the tower fluxes from (A and B) to the full year. The tower data for CH\({}_{4}\) (B) trends to a maximum positive value of \(\sim\)0.045 gCH\({}_{4}\) m\({}^{-2}\) y\({}^{-1}\) in 2014, followed by net negative fluxes in 2015 and 2016, resulting in net negative flux with CO\({}_{2}\) of \(-\)10.8 CO\({}_{2}\)e. Soil chamber data for CH\({}_{4}\) and CO\({}_{2}\) do not reflect tower data. The N\({}_{2}\)O soil chamber data are net negative, resulting in a net positive flux for the soil areas sampled of 25.7 CO\({}_{2}\)e [38], based on the limited chamber data available. Mean CO\({}_{2}\) and CH\({}_{4}\) measured at the three towers were \(-\)10.76 and \(-\)0.03 metric tons per hectare, respectively, while CO\({}_{2}\), CH\({}_{4}\) and N\({}_{2}\)O mean measured at the soil chambers were 25.8, \(-\)0.13 and \(-\)0.0037 metric tons per hectare, respectively. Note, however, that the Howland forest is consistently a sink for CH\({}_{4}\) and N\({}_{2}\)O for the limited periods observed.
## 3 Shearer Positioning System Based on SINS
### Motion Characteristics of the Shearer  
Due to the limitation of the underground operating environment, the shearer can only operate in narrow space. At the same time, the working characteristics of the shearer also lead to the simple motion process. During the mining process, the shearer runs on the track of the scraper conveyor along the working face, and, vertically to the working face, the shearer is moved by the push of the scraper conveyor.  
Figure 4: Principle of the shearer positioning system based on SINS.  
The starting position of the shearer is on the roadway, and then the shearer cuts the coal along the working face. The common sumping modes of the shearer are inclined sumping at the end or in the middle of the working face. If we take the inclined sumping at the end of the working face as an example, the motion process is shown in Figure 5. It can be seen from the figure that the movement of the shearer mainly has two states: coal cutting along the working face and inclined-sumping coal cutting. This allows us to simplify the shearer movement into two states: linear motion and bending motion.
# Fast Scalable Architecture of a Near-ML Detector for a MIMO-QSM Receiver
## 4 Proposed Hardware Architecture for the Digital QSM Detector
### Det2 Module  
The second detection process, det2, was at the same time divided into two parts representing the _for_ cycles in Lines 2 and 11, respectively, of Algorithm 2. Figure 5 shows the first part (det2_p1) with the first norm blocks and the second part (det2_p2) with the last block. When det2_p1 ended, det2_p2 immediately started. Table 5 shows the inputs and outputs of the module.  
Modules:  
* Norm (norm): operation of Euclidean distance.
* RegisterArray for det2 (ra_det2): register array for the results of norm in det2_p1.
* SortingRegs_SP (sregs_SP): register array for the sorting results in det2.
* SortingRegs_SP2 sregs_SP2: register array for the sorting indexes results in det2.
* RearrangerP(rp): rearrangement for the sorting indexes.  
After the calculations with the norm modules were done, ra_det2 sent its stored data to sort. When the data came back from the module, they were split into the data vector and index vector to be stored in sregs_SP and sregs_SP2, respectively.  
sregs_SP was a register array that had both serial and parallel inputs and outputs and stored the sorting data. It also had outputs like _data1_out_ that fed the control module, equivalent to \(\mathbf{d}_{3}(m+1)\) in Algorithm 2, Line 19.  
sregs_SP2 stored the indexes given by the sort module. It only had a parallel input, but serial and parallel outputs; this configuration helps when you need to reorder all indexes (parallel output, as in Line 9 of Algorithm 2) or you need to read only one index.  
In det2_p2, another norm module was used, and its result was added to one of the already stored in sregs_SP, then fed back to the same register and sent to sorting again, emulating the behavior of Lines 14 and 15 of Algorithm 2.  
When coming back from sorting, rp rearranged the old indexes stored in sregs_SP2 based on the new ones and replaced them. For example, if the stored vector was \([3,1,2,4]\) and the new indexes were \([2,1,4,3]\), then the rearranged vector would be \([1,3,4,2]\).  
Figure 5: Architecture of det2.
# Cross-Layer Optimization for Heterogeneous MU-MIMO/OFDMA Networks
## 4 Optimization Framework
### Constraints on MU-MIMO and OFDMA Operation  
In this subsection, we explain the constraints on the operations related to MU-MIMO and OFDMA transmission.
## 3 Results and Discussion
### Criteria for the Evaluation of the Participants' Performance
#### 3.3.1 The MIR Method  
For the MIR method, the standard deviation for the evaluation of the proficiency for the PT rounds conducted,\(\hat{\sigma}\), was established based on the accuracy data provided by the manufacturer of the equipment, Foss, since the previous version of the ISO 8196-3 standard [8] did not provide any accuracy data for the freezing point. Regarding the current situation, the new version of the ISO 8196-3 standard, issued in 2022 [21], provided precise data for repeatability and reproducibility, but not for accuracy, so that \(\hat{\sigma}\) could not be estimated based on the calibration error (by comparing the MIR method against the thermistor reference method). Therefore, we suggest to keep the 4 m\({}^{\circ}\)C value based on the manufacturer's recommendations.
## 3 Results
### Spatial and Temporal Characteristics
#### 3.2.2 Spatial and Temporal Characteristics of ET  
This study statistically analyzed the annual and growing season (from May to September) multi-year average evapotranspiration (ET) changes in the alpine grasslands of Qinghai Province from 2001 to 2018. Figure 6 showed the multi-year average ET spatial distribution map for the alpine grasslands of Qinghai Province. The entire study area showed certain spatial variation in ET, with high ET values concentrated around the Yangtze River source, Lancang River, and the Yellow River source. Low ET values were mainly distributed near the Kunlun Mountain range and the southern part of Qinghai Lake. The regions with higher evapotranspiration (ET) were located in the Sanjiangyuan area, where the surrounding soil moisture content was high, leading to higher evapotranspiration.  
From the spatial distribution maps of the annual and growing season average evapotranspiration (ET), it could be observed that the annual ET values range from 250 to 550 mm, accounting for more than 90% of the area, with 68.4% of the region having ET values between 300 and 450 mm. The eastern region of Qinghai Lake had relatively low annual average ET, ranging from 200 to 300 mm, as shown in Figure 6a. The spatial distribution of ET during the growing season was generally consistent with that of the entire year. The higher ET values were found near the sources of  
Figure 5: Interannual variation in net primary productivity: (**a**) time series, (**b**) monthly average values over multiple years.  
Figure 6: Spatial distribution of mean annual evapotranspiration: (**a**) annual, (**b**) growing season.  
Yellow Rivers, with ET ranging from 250 to 350 mm, while the southern part of Qinghai Lake showed relatively low ET, ranging from 100 to 200 mm.  
This study analyzed the annual ET in the Qinghai Plateau alpine grasslands from 2001 to 2018 and examined the interannual variation trends, as shown in Figure 7a. From the figure, it can be observed that both the annual and growing season ET showed an increasing trend, albeit with fluctuations, and the trends were consistent for both the entire year and the growing season. The interannual mean ET values ranged from 336.8 mm to 439.2 mm, with the highest value occurring in 2017 and the lowest in 2004. The mean annual ET during the growing season was approximately 150 mm lower than the overall annual value, indicating that soil evaporation occurs to a certain extent in the other months of the year in the alpine grassland areas. Figure 7b showed the multi-year average monthly ET for the Qinghai alpine grassland from 2001 to 2018. As shown in the figure, there was a certain amount of evapotranspiration in the alpine grassland every month. In November, December, and January, the evapotranspiration was around 20 mm. Starting from April and May, ET began to increase, peaking in July at 48 mm. From August to October, as the temperature began to decrease and vegetation entered the senescence period, evapotranspiration gradually declined.