esa-sceva/satcom-chunk-collection huggingface dataset:
# In-Situ Seawater Gamma Spectrometry with LaBr\({}_{3}\) Detector at a Nuclear Power Plant Outlet
## 3 Results  
### Calibration  
#### 3.1.1 Energy Calibration  
The purpose of the energy calibration is to determine the relationship between the particle energy and the channel position in the PMT for subsequent analysis of the obtained energy spectrum. The LaBr\({}_{3}\) crystals naturally contain the radioactive isotope \({}^{138}\)La. In addition, because the lanthanides are chemically similar to the actinides and cannot be easily separated, a certain amount of the radionuclide \({}^{227}\)Ac is also present within LaBr\({}_{3}\) crystals. Both \({}^{138}\)La and \({}^{227}\)Ac contribute to the intrinsic radiation of the LaBr\({}_{3}\) crystal and form peaks in the background energy spectrometry. However, these peaks do not overlap with the peaks of the primary radionuclides discharged from Ningde NPP in the liquid form listed in Section 2.3. In the meantime, the peaks of the LaBr\({}_{3}\) intrinsic radiation can help perform energy calibration of the detector and self-stabilizing spectrum correction.  
The effect of \({}^{227}\)Ac on the background spectrum is mainly in the high-energy end, and it arises from a series of alpha decays of its parent and daughters. There are two pathways of decay for \({}^{138}\)La, one by electron capture to generate \({}^{138}\)Ba and the other by beta decay to \({}^{138}\)Ce. The process of electron capture is accompanied by the emission of characteristic X-rays, with a 63.7% probability of emitting K\({}_{\alpha}\) X-rays at 31.84 keV, followed by the Osher effect to generate a 5.6-keV oscillator electron; a 27.5% probability of L\({}_{\alpha}\) X-rays with an energy of 4.5 keV; and an 8.8% probability of M\({}_{\alpha}\) X-rays with an energy of about 1 keV. The Osher electrons and X-rays will undergo a coincidence summing, forming a characteristic X-ray peak in the spectrum with an energy of about 36 keV. \({}^{138}\)Ba emits \(\gamma\)-rays with an energy of 1436 keV when it transits from the excited to the steady state and undergoes a coincidence summing with the characteristic X-rays, forming characteristic peaks in the spectrum with energies of 1441 keV and 1472 keV, respectively. During the decay of \({}^{138}\)La to form \({}^{138}\)Ce, \(\beta\)-electrons are generated with a maximum energy of 255 keV. The excited state of \({}^{138}\)Ce leaps to the steady state and emits \(\gamma\)-rays with an energy of 789 keV. The \(\beta\) electrons and \(\gamma\) rays also undergo coincidence summing and form a continuous spectrum in the spectrum with energies of 789-1044 keV. Based on the above analysis, the 36 keV, 1441 keV, and 1472 keV characteristic peaks of \({}^{138}\)La were selected to calibrate the energy-channel position relationship.
## 4 Discussion
Moreover, the SOD level was significantly decreased in the LPS-induced group of mice compared with the control group, whereas extract pretreatment markedly reversed SOD enzyme activity in comparison to the group administered LPS only. In this regard, an experiment based on leaf extract reported that catalase, SOD and GPX decreased significantly in a CCI4-treated group of mice liver tissues. Moreover, pretreatment with an extract (500 mg/kg) dose resulted in the significant increase in levels of antioxidant enzymes [19]. Moreover, a study based on _Portulaca olercea_ extract reported that the extract significantly controlled the expression of TNF-\(\alpha\), as well as IL-6, compared with the LPS group [50]. After administration of LPS, inflammatory cell infiltration, congestion and hemorrhage was noticed in lung tissues. However, extract treatment noticeably alleviated the lung tissue changes induced by LPS. Another study was in accordance with the current study, as the study, based on 3,5-dihydroxy-4\({}^{\prime}\),7-dimethoxyflavone isolated from _Tamaria aphyllla_ L., reported that this flavone inhibits liver injury [19]. Several proinflammatory mediators such as IL-6 and TNF-\(\alpha\) play a significant role in inflammation. Moreover, cyclooxygenase 2 (Cox-2), a vital enzyme, plays a role in inflammation and increased Cox-2 expression as a feature reported to be common to cancers [51] ([PERSON] et al., 2005). In the current study, it was reported that Cox-2 protein expression was increased in lung tissues of the LPS-induced group compared to the control group. In the LPS + extract-treated group, the expression pattern of the Cox-2 protein was low compared to the LPS-treated group. A previous study was in accordance with the current findings, as Cox-2 was expressed immensely in the LPS model group [52]. Another study based on western blotting reported that Cox-2 expression was significantly higher in the LPS group than in the control group [53]. In this regard, previous findings designated that Cox-2 protein distribution was high after 24 h LPS administration, whereas pretreatment with quercetin significantly prevented the increase in Cox-2 expression [54]. In the current study, it was reported that, when compared with the control group, TUNEL positivity increased in the LPS-treated group significantly. Compared with the LPS group, pretreatment with the extract also showed an increase in the number of TUNEL-positive cells. A previous study was in accordance with the current study, as the number of TUNEL-positive cells was remarkably increased in mice lungs after LPS induction, whereas the number of TUNEL-positive endothelial cells decreased in the lungs of ruscogenin treatment groups [55]. Moreover, increased proportions of TUNEL-positive cells were seen in the LPS group compared with the vehicle group, whereas Capsaicin pretreatment significantly decreased the percentage of TUNEL-positive cells [56].
# Weighted Cluster-Range Loss and Criticality-Enhancement Loss for Speaker Recognition
## 4 Experiments and Results
### Results
#### 4.4.2 Evaluating Weighted Cluster-Range Loss  
The only difference between cluster-range loss and weighted cluster-range loss is the weighting factors imposed on the loss function. Intuitively, cluster-range loss is a special case of weighted cluster-range loss, with all the weighting factors being one. Weighted cluster-range loss absorbs the advantages of weighted triplet loss and cluster-range loss. The cluster-range loss focuses on the hardest samples, while the idea of weighted triplet loss is to impose different weights on the terms of the loss function. By combining these two ideas, our proposed weighted cluster-range loss could not only converge in a more efficient way by hard sample selection, but also pay more attention to the negative samples and thus help to correctly classify those indecisive embeddings. As is presented in (a) and (c) of Figure 9, some clusters of WCRL have a larger range than that of CRL (especially the cluster in light blue), due to the bias-weight of WCRL. Finally, when using the Res-DA architecture, the weighted cluster-range loss further improves the top-1 accuracy of cluster-range loss by 2.0% on the VoxCeleb1 dataset and by 1.0% on the CN-Celeb dataset.  
By imposing the weighting factor, the networks will prioritize the decreasing of cosine similarities between anchors and negatives, and those indecisive samples will be more likely to be pushed  
Figure 9: Visualization of features learned with different loss configurations.  
away from the incorrect categories. When applying \(\omega_{1}\)(\(>\)1) and \(\omega_{2}\)(\(>\)1) simultaneously or solely applying \(\omega_{2}\)(\(>\)1), although \(s_{a,n}\) has a relative higher weight than that of \(s_{a,p}\), the speed of shrinking the distance of positives gets lower, which is however also important for the identification performance. Therefore, it seems that keeping \(\omega_{2}=1\) and only changing the value of \(\omega_{1}\)(\(>\)1) may lead to better performance. Note that there might be an optimal value for \(\omega_{1}\), but it is still obtained by trial and error for the moment. Let \(\omega_{1}=1+\eta\), then \(\eta\) is a relatively small number and is suggested to be 0.0002-0.0005. There is a subtle balance between the decrease of \(s_{a,n},h^{n}\) and the increase of \(s_{a,p},h^{p}\), and thus, \(\omega_{1}\) should not be set too large. The results shows the effectiveness of weighting, while also indicating that imposing a positive-suppressed or positive-attentive weighting factor, i.e., \(\omega_{2}\
eq 1\), may not lead to a satisfactory result.
## 1 Introduction
In this study, we used observations at eight sites of ChinaFLUX to evaluate the accuracy of MODIS-GPP and -ET. The objectives of the study are: (1) to assess the differences of MODIS-GPP and -ET with flux observations, respectively; (2) to analyze the performances of MODIS-GPP and -ET in different land cover types over China; and (3) to analyze the impacts of meteorological data and remote sensing input data on the model results.
# Compact Midwave Imaging System: Results from an Airborne Demonstration
## 2 Materials and Methods
### Instrument
Finally, the band at 2.25 \(\upmu\)m, which is dominated by solar reflection, provides high-spatial-resolution cloud reflectivity data that will aid in the determination of AMVs,  
Figure 1: (**a**) Conceptual diagram (adapted from [5]) depicting the ground footprint resulting from a pushframe imager with a single lens assembly and a five-stripe filter mounted on the focal plane array; line colors are used to aid in following individual ray paths; (**b**) Stripe filter configuration to provide fore, nadir, and aft views at 3.75 \(\upmu\)m for stereo imaging with bands at 4.05 and 2.25 \(\upmu\)m for multispectral analysis. Figure 2: (**a**) Snapshot from CMIS engineering flight showing unprocessed counts resulting from the five-stripe filter. (**b**) Depiction of the fore, nadir, and aft views as the satellite (or aircraft) moves from right to left to derive apparent changes of position for clouds or terrain. cloud/surface phenomenology discrimination and removal of the daytime solar signal from the 4.05-\(\upmu\)m band. The 2.25-\(\upmu\)m band is also useful for detecting hot wildfires with fewer saturated pixels than the other two bands, due to blackbody physics and a narrow bandwidth. The 3.75-\(\upmu\)m band was chosen to collect multi-angle observations for CMS for several reasons. In general, multiple choices within the midwave infrared (MWIR; 3-5 \(\upmu\)m) or longwave infrared (LWIR; 7-14 \(\upmu\)m) spectrum are capable of providing day/night observations, which are needed to meet science requirements specified in the 2017-2027 ESAS. To meet sensitivity requirements, the CMS detector requires cooling to 150 K at 3.75 \(\upmu\)m, which is much warmer than that required for LWIR wavelengths (e.g., 12 \(\upmu\)m). A relatively warm operating temperature is important for keeping the overall power draw low for the instrument. Finally, a band at 3.75 \(\upmu\)m provides higher spatial resolution than a potential 12-\(\upmu\)m band on a pixel-by-pixel basis for the same size lens due to the smaller diffraction blur at shorter wavelengths. The spatial improvement is nominally \(\sim\)3\(\times\), since 3.75 \(\upmu\)m is more than a factor of three shorter than 12 \(\upmu\)m.  
Midwave-infrared channels (3-5 \(\upmu\)m) have become a staple of Earth remote sensing with proven capabilities on GOES, POES (AVHRR), JPSS (VIIRS), and Terra/Aqua (MODIS), among others. VIIRS and MODIS use HgCdTe detectors that require cooling down to cryogenic temperatures (80 K). MISR relies on an uncooled CCD which acquires images in the visible and provides a stereo capability like CMIS. Table 1 shows the comparison between measured performance for CMS and specifications for VIIRS, MODIS, MISR, and ASTER.
## 4 Experimental Results and Discussion
### Wavefront Compensation with the Main Amplifiers in Operation  
Depending on the perfect DM surface achieved in the wavefront compensation experiment without the main amplifiers in operation, the output wavefront with the main amplifiers in operation is measured when the laser system operates at a 40 J level with a 3 ns pulse duration, as an example. Figure 5a shows that the initial output wavefront distribution with a PV value of 0.86 \(\lambda\) and an RMS value of 0.14 \(\lambda\). This wavefront is not as good as the one without the main amplifiers in operation, because of the wavefront distortion of the pumped dynamic thermal distortion and residual heat in the laser system. Therefore, it is necessary to compensate the distortion by the wavefront shaping method introduced in Section 2. In terms of this experiment, the laser system operates at a single-shot low repetition rate of approximately 2 shots per hour. As the iterative times increase in the following several shots, the output wavefront tends to flatten. In the wavefront shaping process, the best wavefront distribution of the final output laser is shown in Figure 5b with a PV value of 0.29 \(\lambda\) and an RMS value of 0.06 \(\lambda\), respectively.  
The flattening wavefront distribution is achieved after the wavefront shaping by using the front-stage small-aperture DM in the high-power laser system. Figure 6 shows the output wavefront PV and RMS value change in the shaping process. Based on several iterations compensation, the output wavefront is corrected to flatten with a PV value of less than 0.4 \(\lambda\) and an RMS value of about 0.06 \(\lambda\), respectively. As a whole, with the increase in the iterative time, the PV and RMS values gradually decrease. When the PV value is small enough (less than 0.5 \(\lambda\)), with the increase in iterative time, the output wavefront PV and RMS values have a slight fluctuation, which is caused by the randomness of the dynamic wavefront distortion and operation stability of the high-power laser system.  
Figure 5: The output wavefront of the wavefront shaping with the main amplifiers in operation. (**a**) The initial output with DM; (**b**) the final output.  
Figure 6: The output wavefront PV and RMS values during the process of wavefront compensation with the main amplifiers in operation.  
From the results of the wavefront-shaping experiment, the output wavefront can be achieved as the flattening distribution by the wavefront compensation without the main amplifiers in operation, but the output wavefront with the main amplifiers in operation is not necessarily good because of the wavefront distortion caused by the main amplifiers. By wavefront shaping with the main amplifiers in operation, the output wavefront meets the expectation with a PV value of less than 0.4 \(\lambda\). The 1053 nm flattening wavefront laser can be injected into the frequency conversion module of the high-power laser system for the third harmonic generation.
## 4 Performance Comparison
As a proof, the mixed rectifier, constituted by cross-coupled and active diode proposed in [46] allows functions in a wide input voltage range due to the adaptive configuration of the rectifier. Figure 24a reports \(PCE\) versus the maximum output power density, defined as the ratio between the maximum output power and area occupation for all the solutions in Table 1. It is apparent that [51] shows the best performance due to the higher value of \(PCE\) and output power density, while occupying much more area than [45]. High power efficiency of solution in [51] is reached thanks to an on-off delay compensation implemented through a digital control circuit. As claimed by the authors, [PERSON] et al., the comparator and logic losses affects the total power breakdown for less than 1%, allowing obtaining a reduction of the losses, associated to rectifier, limited in the range of some percentile points. The achieved results prove that the use of digital-based circuitry to control switching activity of the active diodes is a captivating solution for high-power super-threshold IMD applications. Figure 24b shows the maximum output power density versus the input voltage. By inspection of this figure, it is apparent that the solution reported in [46] achieves a low value of maximum supply voltage (1.1 V) but it has the lowest output power density. Consequently, its performance is lower than [35; 49; 51]. The best performance is therefore achieved by the solutions proposed in [35; 49; 51].
# On Channel Estimation in LTE-Based Downlink Narrowband Internet of Things Systems
## 4 NB-IoT Downlink Channel Estimation
### LMMSE Estimation  
The channel response at the NRS location while deducting the mean square error (MSE) of the real value and the estimated value is obtained by  
\[\boldsymbol{\Psi}_{LMMSE}=\boldsymbol{\Phi}_{\boldsymbol{\Psi}\boldsymbol{ \Psi}}(\boldsymbol{\Phi}_{\boldsymbol{\Psi}\boldsymbol{\Psi}}+\sigma^{2}{( \mathbf{A}\mathbf{A}^{H})}^{-1})^{-1}\boldsymbol{\Psi}_{LS} \tag{14}\]  
where \(\boldsymbol{\Phi}_{\boldsymbol{\Psi}\boldsymbol{\Psi}}=E[\boldsymbol{\Psi} \boldsymbol{\Psi}^{H}]\) is the autocorrelation matrix of size \(N\times N\) of the channel at the NRS positions, \(\sigma^{2}\) is the added noise variance of the channel, which is defined by \(\beta/SNR\) (where the value of \(\beta\) is a constant and depends on signal constellation). For the case of NB-IoT downlink, QPSK modulation is used, and the value of \(\beta\) is 1. Furthermore, \(\boldsymbol{\Psi}_{LS}=\mathbf{A}^{-1}\mathbf{B}\) is the LS estimation of \(\boldsymbol{\Psi}\) which can be defined as  
\[\boldsymbol{\Psi}_{LS}=\mathbf{A}^{-1}\mathbf{B}=\left[\frac{B(0)}{A(0)}, \frac{B(1)}{A(1)},\ldots,\frac{B(L)}{A(L)}\right]^{T} \tag{15}\]  
When the OFDM symbol changes in \(\mathbf{A}\), the LMMSE estimation in Equation (14) needs to inverse the matrix every time, and hence it possesses large computational complexity. It can be reduced by averaging the estimator over all transmitted data [30]. Therefore, we can replace the term \({(\mathbf{A}\mathbf{A}^{H})}^{-1}\) by its expectation \(E\Big{\{}{(\mathbf{A}\mathbf{A}^{H})}^{-1}\Big{\}}\). Hence, the quantity \({(\mathbf{A}\mathbf{A}^{H})}^{-1}\) turns into an identity matrix \(\mathbf{I}_{L}\). Therefore, the LMMSE estimation from Equation (14) becomes  
\[\boldsymbol{\Psi}_{LMMSE}=\boldsymbol{\Phi}_{\boldsymbol{\Psi}\boldsymbol{ \Psi}}(\boldsymbol{\Phi}_{\boldsymbol{\Psi}\boldsymbol{\Psi}}+\sigma^{2}{( \mathbf{I}_{L})})^{-1}\boldsymbol{\Psi}_{LS} \tag{16}\]  
Now, \(A\) is not the part of the matrix calculation anymore and hence the term \(\sigma^{2}{(\mathbf{I}_{L})}\) does not need to be evaluated when the value of \(\mathbf{A}\) changes each time. If \(\boldsymbol{\Phi}_{\boldsymbol{\Psi}\boldsymbol{\Psi}}\) and SNR are set to a fixed value, the matrix \(\boldsymbol{\Phi}_{\boldsymbol{\Psi}\boldsymbol{\Psi}}(\boldsymbol{\Phi}_{ \boldsymbol{\Psi}\boldsymbol{\Psi}}+\sigma^{2}{(\mathbf{I}_{L})})^{-1} \boldsymbol{\Psi}_{LS}\) will be calculated once only but if the corresponding values of \(\boldsymbol{\Phi}_{\boldsymbol{\Psi}\boldsymbol{\Psi}}\) and SNR change, then it needs to be calculated again. In reality, it is very difficult to further calculate the result by using Equation (16) because of the large size of the channel correlation matrix \(\boldsymbol{\Phi}_{\boldsymbol{\Psi}\boldsymbol{\Psi}}\). Hence, to overcome this difficulty of high complexity, we apply the singular value decomposition (SVD) technique to get a low ranked channel autocorrelation matrix \(\boldsymbol{\Phi}_{\boldsymbol{\Psi}\boldsymbol{\Psi}}\) according to [30] as given below.
# Finite Element Analysis of Combined Energy Piles with Long and Short Heat Exchanger Tubes
###### Abstract  
To improve the heat exchange effect of energy piles in coastal areas, a new energy pile with a combination of long and short heat exchanger tubes is proposed. This technology combines the characteristics of implanted pile construction and arranges heat exchanger tubes of different lengths inside and outside the precast pipe pile, which can make full use of the geological conditions in coastal areas. Finite element analysis was applied for a project in a deep, soft soil ground to study the effectiveness of the new combined energy pile technology. The influences of the combined heat exchanger tubes and groundwater seepage conditions on the heat transfer and stress state of the energy pile were analyzed. The results show that the deformation and internal force of the pile body are closely related to temperature change. The temperature change is determined by heat transfer, which is closely related to the arrangement of heat exchanger tubes and underground water flow. With the increase of groundwater seepage velocity, the heat taken away by the heat exchanger tubes gradually increases; thus, the heat exchange between the heat exchanger tubes and the pile body decreases. The inner heat exchanger tube of the pile leads to an increase in heat exchange. However, as the length of the inner heat exchanger tube increases from 40 m to 80 m, the heat exchange decreases. The research results provide technical support for further development of the new energy pile technology.  
combined energy pile; heat exchanger tubes; groundwater seepage; numerical simulation; mechanical performance; implanted pile
## 4 Q3: What Are the Methodologies Used to Overcome the Challenges in UIoT?
### Methods to Overcome the Technical Challenges of UIoT
#### 4.1.7 Device Management and Physical Damage Protection Methods  
In [102; 103], the existing techniques for solving the device management issues in UIoT are discussed, and some methods are indicated herewith. In the case of device management, in ISO/IEC 30140-1, fouling cleaners and housing cases shall be used for cleaning marine wild animals attached to underwater devices, waterproofing and construction of underwater sensor nodes resistant to high water pressures, respectively. In addition, as a functional requirement for underwater device management, identification of available resources and status of the devices are suggested in ISO/IEC 30142. In [26], [PERSON], [PERSON] et al. designed and developed the underwater network management system (U-NMS). The proposed system enables automatic software updates and monitoring of underwater devices using fault, configuration, accounting, performance, security and constrained management (FCAPSC) functions of U-NMS for physical damage protection.
# Mars to Earth Data Downloading: A Directory Synchronization Approach
## 5 Experiment Description
### Status Reports, Data Collection and Processing  
Bundles have no size limits and are generally larger and fewer than IP packets. They can be tracked in tests by status reports, which are extremely valuable for performance analysis. A status  
Figure 7: The layout of the Virtualbricks testbed used in experiments.  
report is an administrative bundle containing information about the time when a specific data bundle is either received, forwarded, taken into custody, deleted, or delivered [5]. The source application can enable the generation of status reports by setting the corresponding request flags in the bundle primary block. Each status report always contains the source EID (End point IDentifier) and the generation timestamp of the data bundle to which it refers, as well as one or more additional timestamps. Status reports are collected by the DTNperf monitor into a csv file for further analysis. This file is then processed by a data-sheet application, to extract the desired information and plot the graphs.