esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Ethical Considerations
### Data Privacy  
Our study involved analyzing feedback data from digital deliberation platforms, which might contain personal and sensitive information. To protect user privacy, we anonymized all personal data. Compliance with data protection regulations, such as the General Data Protection Regulation (GDPR), is essential, to safeguard users' personal information and maintain their trust in digital deliberation systems.
## 2 Materials and Methods  
### Materials  
SA suitable for cell immobilization in powder form (code 71238), low viscosity SA in powder form (code A1112) and medium viscosity SA in powder form (code A2033) were purchased from Sigma-Aldrich (Milan, Italy). The SAs used are further indicated by a \"SA(M\({}_{w}\))MG(##)\" string, in which M\({}_{w}\) is the molecular weight (in kDa) and MG is the M/G ratio; the MG value ## is given in numbers without dermal punctuation. Information regarding the molecular weight and M/G ratio of SA427 MG07 and SA495 MG19 was obtained through gel permeation chromatography (GPC) and 1H-NMR, respectively, as reported by [PERSON], et al. [24]. Information regarding the molecular weight and M/G ratio of the SA51 MG03 was obtained with chromatographic techniques, as reported by [PERSON] et al. [25]. Poly(ethylene oxide) (PEO) in powder form with a molecular weight of 1000 kDa (code 372781), liquid Triton X-100 (Lot#110M0009V) and agar were also purchased from Sigma-Aldrich. Absolute ethanol was bought from Bio-Optica (Milan, Italy)  
Probiotics (Probioti, 60 capsules) were purchased from OTI--Officine Terapie Innovative s.r.l. (Carsoli, AQ, Italy). In 4 capsules, they are reported to contain a mixture of the following live probiotic strains in colony forming units (CFUs): _Lactobacillus plantarum_ BG112 > 10 bn CFUs, _Streptococcus thermophilus_ SP4 > 5 bn CFUs, _Lactobacillus cagi_ BGP93 > 5 bn CFUs, _Lactobacillus acidophilus_ LA3 > 2 bn CFUs, _Bifidobacterium breve_ SP85 > 1 bn CFUs, _Bifidobacterium infantis_ SP37 > 1 bn CFUs, _Bifidobacterium longum_ SP46 > 1 bn CFUs and _Lactobacillus bulgarcius_ SPS > 0.25 bn CFUs.  
Human Keratinocyte (HaCaT) cell line was obtained from CLS-Cells Line Service (Eppelheim, Germany). Fetal Bovine Serum (FBS) was purchased from Thermo Fisher Scientific (Waltham, MA, USA). Dulbecco's Phosphate Buffered Saline (DPBS), Dulbecco's Modified Essential Medium (D-MEM), L-Glutamine, penicillin and streptomycin were purchased from Sigma Aldrich. AlamarBlue(r) was purchased from Thermo Fisher Scientific. De Man-Rogose-Sharpe agar (MRSA) and Tryptic Soy Broth (TSB) were obtained from Oxoid Ltd., Cheshire, UK. _Escherichia coli_ ATCC 25922 was purchased from ATCC (Manassas, VA, USA).
# Upper-Bound Limit Analysis of Ultimate Pullout Capacity of Expanded Anchor Cable
## 3 Upper-Bound Solution of Ultimate Pullout Capacity of Expanded Anchor Cable
### Ultimate End Resistance Calculation
#### 3.2.1 Power Calculation of Logarithmic Spiral Region  
In the logarithmic spiral region, through the calculation method of elementary surfaces and volumes generated by adjacent points [27], the elementary rates of work and rates of internal energy dissipation were obtained. Finally, the computation of the rate of work was achieved by the summation of the elementary rates of work, and the calculation of the internal energy dissipation was carried out by the summation of the elementary energy dissipations along the different elementary surfaces. The rate of work of the soil weight and the rate of internal energy dissipation of the velocity discontinuity surfaces in the logarithmic spiral region could be finally calculated as follows [20]:  
\[\begin{array}{l}\dot{W}_{\gamma}\\ \par\par=\iint_{V}\stackrel{{\rightarrow}}{{\gamma}}\cdot \stackrel{{\rightarrow}}{{v}}dV=\Sigma_{ij}(\stackrel{{ \rightarrow}}{{\gamma}}\cdot\stackrel{{\rightarrow}}{{v}}_{ij }\cdot V_{i,j}+\stackrel{{\rightarrow}}{{\gamma}}\cdot \stackrel{{\rightarrow}}{{v}}_{ij}\cdot V^{\prime}_{i,j})\\ \par\par=\omega\gamma\underset{i,j}{\Sigma}(R_{i,j}V_{i,i}\sin\beta_{i,j}+R ^{\prime}_{i,j}V^{\prime}_{i,j}\sin\beta^{\prime}_{i,j})\\ \end{array} \tag{11}\]  
\[\begin{array}{l}\dot{W}_{D}\\ \par=\iint_{S}cv\cos\varphi dS=\underset{i,j}{\Sigma}(cv_{ij}S_{i,j}\cos\varphi +cv^{\prime}_{i,j}S^{\prime}_{i,j}\cos\varphi)\\ \par=\omega c\cos\varphi\underset{i,j}{\Sigma}(R_{i,j}S_{i,j}+R^{\prime}_{i,j}S^ {\prime}_{i,j})\\ \end{array} \tag{12}\]  
In contrast to the blow-out failure mechanism of the shield tunnel face described in [20], which presented only one type of failure model wherein the velocity discontinuity surfaces all intersected with the ground surface, two typical failure models for the logarithmic spiral region are shown in Figure 8 because the coordinates of the rotation center point O differed, and the potential slip surface was also considered. In the first model, the boundary lines AN and BM of the velocity discontinuity surface in the vertical plane intersect with the ground surface at point N and with the potential slip surface at point M, respectively (Figure 9a); in the second model, the logarithmic spirals AN and BM both intersect with the potential slip surface (Figure 9b).
## 1 Introduction
### Aim and Objectives  
The objective of the present paper is to assess and report on the state of Armenia's forests with a focus on the Dilijan National Park (See Figure 1). This park is one of the four National Parks in Armenia, located in the Northern part of Armenia on the slopes of the Pambak, Miapor and Aregani mountain chains and on the basin of Aghstev and Getik rivers. Although the area became a specially protected area in 1958 in order to protect the park's flora and fauna, the National Park was only established in the year 2002. Today, the Dilijan National Park is known for its forest landscapes, rich biodiversity, medicinal mineral water springs, natural and cultural monuments, and extensive network of hiking trails. We proved the suitability of validated EO products for an independent forest monitoring and support land use planning for sustainable forest management. Two main tasks have been performed: (1) providing detailed land use and land cover classification maps for the epochs 1991-2019 as a basic planning support tool for characterizing forest ecosystems and main evolution trends and drivers of deforestation in Dilijan National Park, Armenia, and (2) delivering an in-depth examination of the forest classes of the previous service, subdividing forested areas into different forest density and type classes. The service shall focus on assessing and locating areas where deforestation or forest degradation is taking place.
## 4 Experimental Results and Discussions  
We conducted experiments on two widely used datasets: NWPU VHR-10 [37] and Pascal VOC [38]. First, we compare the proposed SSD7-FFAM with other state-of-the-art methods on the NWPU VHR-10 dataset containing many small targets. Second, we conducted ablation experiments on the Pascal VOC dataset to understand the role of each module in the proposed FFAM. Finally, we added the proposed FFAM to the original SSD300 to form SSD300-FFAM and carry out comparative experiments with other state-of-the-art detectors on the Pascal VOC dataset to verify the effectiveness of the FFAM. We adopted the standard mean Average Precision (mAP) to measure the object detection performance.
## 3 Automatic Language Identification Using Speech Rhythm Features
### Automatic Language Identification Using Rhythm Metrics
#### 3.4.1 R-Vector-Based LID with SVM  
The SVM aims to find support vectors in the decision boundary. These vectors are modeled with different types of kernel functions, such as a linear kernel and a Gaussian kernel. The function is dependent on a domain and the dimension of features. As R-vector is a multi-dimensional feature, a Gaussian kernel, namely, the radial basis kernel, is used. Using the R-vector, the support vectors in the kernel function are trained. After the training session, the distance between the support vectors of each language and input data to be classified are calculated with a Gaussian kernel. A language indicating the least distance values is determined as a result. An advantage of the proposed approach employing SVM is its scalability. Specifically, once an interval detection module is prepared, it is relatively easy to identify an open-set language, extending various types of languages. On the other hand, the conventional methods such as i-vector or CNN should firstly train the corresponding language model for feature extraction to identify an open-set language.
## 3 Comparative Analyses Results
### Rain-Gauge vs. Radar Observations
#### 3.1.1 PANTHERE vs. RadOME  
For the April case (Figure 4a), precipitations were mainly located over the south of France, especially over the mountainous areas, as observed by RADOME (Figure 2a). Table 2 presents the rain maximum recorded by the radar and all other rain products used in this study, for each area of interest defined in Figure 2c, as well as the distance between each of these maxima and their RADOME counterpart. Table 2 indicates that PANTHERE recorded a rain maximum of 98.1 mm, 89.0 mm, 69.5 mm, 72.1 mm, and 44.1 mm over the WMC, SAL, DUR, CEVs and JUR areas, respectively. Except over CEV, PANTHERE recorded higher maxima precipitation accumulations than RADOME. The distance between RADOME and PANTHERE maximums was 32.29 km in SAL, 10.41 km over CEV, and 14.18 km over JUR. However, the distance increased to 225.54 km over WMC and to 107.99 km over DUR. Regarding the median precipitation accumulations estimated by PANTHERE (given by Table 3) over the same areas, they were equal  
Figure 3: ECMWF ERA5 synoptic conditions on the 23 April 2022 at 12:00 UTC (**a**) and the 26 June 2022 at 06:00 UTC; (**b**) the temperature at 1000 hPa (in colours), the mean sea-level pressure (hPa) (white solid lines), and the 500 hPa geopotential (in m\({}^{2}\) s\({}^{-2}\), dashed grey lines). to 24.8 mm over WMC, 30.5 mm over SAL, 28.0 mm over DUR, 30.1 mm over CEV, and 25.4 mm over JUR. These values were close to the values observed by RADOME: 26.6 mm for WMC, 31.4 mm for SAL, 23.5 mm for DUR, 22.3 mm for CEV, and 22.6 mm for JUR. Nevertheless, we can notice a trend of overestimating the accumulation in the CEV and SAL regions. Near the Mediterranean coasts, RADOME measured an amount less than 20 mm of precipitation where PANTHERE estimated up to 30 mm of precipitation accumulation. Figure 4c presents the absolute bias calculated comparing PANTHERE and RADOME at each RADOME station (using the method described in Section 2.2) for the April event. This comparison shows that PANTHERE underestimated the rain amounts in the south of the CEV area while it overestimated them in the WMC. We can observe that the main differences between PANTHERE and RADOME were localized over the mountainous areas, especially over the Massif Central, the Cevennes, and the Southern Alps. Table 3 gives the mean absolute bias calculated for each area of interest. PANTHERE had quite similar values compared to the rain gauges because the mean difference was lower than 2.14 mm. There was only one exception for the CEV region, where the mean absolute bias reached +6.89 mm. Figure 4: Precipitation accumulation estimated by PANTHERE from the observations of the ARAMIS radars network at the end of (**a**) the April case and (**b**) the June case. Absolute biases with the RADOME precipitation gauges network using the point-to-pixel approach are also represented for both (**c**) the April case and (**d**) June case.
## 3 Power Allocation Optimization and Problem Formulations
### Training Sequence Design in Downlink FDD Systems  
The formulation in (14) describes the outcome of a channel estimator designed to minimize channel estimation error, and this outcome is contingent on the structure of the correlation matrix at the transmitter. This has led to the exploration of optimizing the training sequence by leveraging the statistical structure of the channel covariance matrix \(\mathbf{H}\). Therefore, \(\mathbf{S}_{\text{tr}}\in\mathbb{C}^{N\times\tau_{\text{tr}}}\) is constructed using the eigenvectors of length \(\mathbf{H}\) equal to \(\tau_{\text{tr}}\). These eigenvectors represent the largest eigenvalues. To this end, the training matrix can be expressed as  
\[\mathbf{S}_{\text{tr}}=\big{[}\mathbf{u}_{1},\dots,\mathbf{u}_{\tau_{\text{tr} }}\big{]}. \tag{15}\]  
A simplified analytical form for the MMSE CSI estimate can be obtained by substituting Equation (15) into Equation (14), which yields  
\[\mathbf{\Theta}=\rho_{\text{tr}}\mathbf{U}_{\text{tr}}\mathbf{\Sigma}_{\text{ tr}}^{2}\big{(}\rho_{\text{tr}}\mathbf{\Sigma}_{\text{tr}}+\mathbf{I}_{\text{tr}} \big{)}^{-1}\mathbf{U}_{\text{tr}}^{\text{H}}. \tag{16}\]Here, \(\mathbf{\Sigma}_{\tau_{\text{tr}}}\in\mathbb{R}^{\tau_{\text{tr}}\times\tau_{\text{tr}}}\) represents the eigenvalues of \(\mathbf{H}\), where the eigenvalues are arranged as follows: \(\sigma_{1}\geq\sigma_{2}\geq\cdots\geq\sigma_{\tau_{\text{tr}}}\). To constrain the downlink training sequence overhead, the channel energy, associated with the eigenvectors \(\mathbf{u}_{\tau_{\text{tr}}+1},\ldots,\mathbf{u}_{N}\) of \(\mathbf{H}\), is omitted in the training sequence construction and consequently excluded from precoding. Using the proposed training sequence design in Equation (16), one can obtain a simplified formulation of the trace of the MMSE estimation as follows:  
\[\text{tr}(\mathbf{\Theta})=\sum_{n=1}^{\tau_{\text{tr}}}\frac{\sigma_{n}^{2}}{ \sigma_{n}+1/\rho_{\text{tr}}}. \tag{17}\]  
The expression in Equation (17) can be further simplified with the \(P\)-DoF model. In what follows, the proposed power allocation optimization procedure will be described.
# Optical Link Design for Quantum Key Distribution-Integrated Optical Access Networks
###### Abstract  
To achieve commercial scalability, fiber-based quantum key distribution (QKD) systems must be integrated into existing optical communication infrastructures, rather than deployed exclusively on dedicated dark fibers. Integrating QKD into optical access networks (OANs) would be particularly advantageous, as these networks provide direct connectivity to end users for whom security is critical. Such integration can address the inherent security vulnerabilities in current OANs, which are primarily based on time-division multiplexing passive optical networks (TDM-PONs). However, integrating QKD into PONs poses significant challenges due to Raman noise and other detrimental effects induced by PON signals, which intensify as the launched power of PONs increases to support higher transmission speeds. In this study, we review recent advancements in both QKD and access network technologies, evaluate the technical feasibility of QKD-OAN integration, and propose cost-effective strategies to facilitate the widespread deployment of QKD in future access networks.  
Keywords:quantum key distribution; optical access network; passive optical network +
Footnote †: journal: _photonics_
## 4 Trends and Challenges
### Multidimensional
#### 4.1.2 Multiband  
The NASA-ISRO Synthetic Aperture Radar (NISAR) as shown in Figure 16 is scheduled to be launched in 2025 [91]. Developed by NASA and ISRO, it is primarily designed to monitor almost all land and ice surfaces on Earth every 12 days. Equipped with a dual-frequency radar system (L-band and S-band), it is used to track vegetation changes, measure ice sheet dynamics, monitor natural disasters such as earthquakes and landslides, and study long-term changes in the Earth's structure and climate [92; 93].  
Figure 15: Norway’s Svalbard islands are covered by ice caps [image credit: ESA].  
China is currently progressing with the testing of multiband SAR systems on airborne platforms and has made significant advancements. [PERSON] et al. [94] developed a representative airborne multi-dimensional SAR observation platform (MSJosSAR), which integrates X, C, and L-band SAR to enable coordinated multi-band imaging. By combining the scattering characteristics of different bands, the system enhances target recognition, penetration, and scene adaptability. A unified frequency source ensures time and phase synchronization across bands, supporting data fusion and interferometric applications. MSJosSAR also supports full-polarization imaging, variable incidence angle observation, and repeat-pass interferometry. Automatic registration across bands is achieved through error modeling and self-calibration, with sub-pixel accuracy. The platform demonstrates strong performance in multi-dimensional structure reconstruction and coherence variation monitoring, laying a solid technical foundation for future satellite-based SAR systems with multi-band, high-resolution, and high-precision capabilities. [PERSON] et al. successfully acquired high-resolution SAR data in the P, L, C, and X bands using a multi-frequency SAR sensor system developed by the Aerospace Information Research Institute, Chinese Academy of Sciences, providing data support for ground target classification and Earth observation applications [95]. [PERSON] et al. designed a compact X/L dual-band, multi-bandwidth receiver scheme to meet the requirements of multi-frequency, multi-mode receivers for UAV platforms. Compared with traditional UAV-borne radar receivers, this design features multi-mode operation, wide signal bandwidth processing capability, and a high level of integration [96].
# Design and Application Space Exploration of a Domain-Specific Accelerator System
## 3 Acceleration in Application Level
### Data Interface Latency Analysis  
Figure 4 shows the transmission rate of different data size range from 0.5 KByte to 64 KByte of RASP. The values are collected by real chip test rather than simulation model test, so the data has great reference value. It's clear that due to the overhead of setting up a transaction, the transmission efficiency is very low when the data amount is small [23], and is gradually trending to saturation as the amount of data is getting bigger. This is because the overheads of setting up a transaction are usually amortized over a large bulk transfer.  
Under such circumstance, and according to the above analysis about accelerator execution time breakdown, to achieve higher efficiency, it is natural that we should avoid small chunks transmission via DMA to the greatest extent. Therefore, we use a large addressable on-chip memory to store the result data of the algorithms, which serves as the source data of the following kernel in the task.
## 1 Introduction
Existing experimental studies focus on the thermal hydraulic characteristics of PCHEs and the construction of related heat transfer and pressure drop correlations. Limited by the small size and high pressure of PCHE channels, there are few experimental observation studies on the flow patterns in PCHE channels at present. Based on the investigation and analysis of the literature, the research on S-CO\({}_{2}\) Brayton cycle power generation is still in the development stage. Studies on flow and heat transfer in PCHE belong to the field of applied research, and most of them focus on the establishment of the heat transfer correlation and the design of PCHEs [26; 27; 28; 29]. Nevertheless, most studies are based on the assumption that the flow in the PCHE channel is evenly distributed, and few studies consider the uniformity of the flow distribution in each channel of the PCHE, PCHE pressure drops, and heat transfer efficiency, mainly because the flow channel is small. The internal flow parameters are difficult to measure. Thus, there are still problems in the study of PCHE heat transfer characteristics. For the straight barrel inlet header PCHE channel, the non-uniform flow distribution phenomenon is caused by the structure itself. If a guide plate is set up in the diversion area, then a higher flow can be obtained in the middle of the channel. Nevertheless, the additional guide plate structure is bound to cause a diversion area with increased pressure drop, leading to deterioration of the PCHE hydraulic characteristics on the whole, thus affecting the turbine inlet pressure in the Brayton cycle, which is a pyrrhic gain. In addition, due to the high operating pressure of the system, the setting of the guide plate may cause local stress concentration, which is not conducive to safe and stable operation of the system. Therefore, the design of the existing straight barrel inlet header PCHE must be optimized to improve the non-uniformity of the flow distribution in each layer of the PCHE so that the flow in the straight barrel inlet header PCHE can be generally evenly distributed. This paper is aimed at the optimization structure of straight barrel inlet header PCHE. When S-CO\({}_{2}\) flows in the PCHE, the structural types and working parameters of the inlet header and diversion zone may lead to differences in the flow distribution in each channel of the PCHE, and the uniformity of the flow distribution increases with increasing PCHE scale. This difference in flow distribution affects the thermal hydraulic characteristics of the PCHE and further reduces the comprehensive performance of the Brayton cycle power generation system. Therefore, a numerical simulation method is proposed to explore the influence of flow uniformity on the overall performance of the PCHE and improve the structure of the straight barrel inlet header PCHE to optimize the S-CO\({}_{2}\) distribution in the PCHE flow channel.
## 3 Proposed Scheme
### Initialization Phase
#### 3.1.2 Modular Addition  
The \(user_{i}\) partitions its own data \(x_{d}^{i}\) into \(k\) partitions denoted as \(x_{(ind_{i}[j],d)}^{i}(1\leq j\leq k)\) and sends them to every pairwise node. However, the partitioned data can be easily guessed, especially with brute search, as the consumption value at every timeslot is very small. For this, we impose extra noise (masking keys) which is only known by pairwise nodes themselves on the partitioned data to further secure the individual data.
## 4 Simulations and Experiments
### Simulations
#### 4.1.2 Simulation Results of Signal Processing  
Figure 6 shows the simulation results of signal process based on Kalman filter. There is obvious noise in the signal processed by DFBGA method due to the simulated maneuvering acceleration. DFBGA and SLAIM method can suppress maneuvering interference effectively. Calculate the RMSE between the signal processed by DFBGA and SLAIM and the reference signal according to Equation (28). Compare with the simulated original-gyro signal, the RMSE of signal processed by DFBGA and SLAIM method decreased 29.08%.  
Figure 7 shows the simulation results of signal process based on phase-lag-free LPF. It can be seen from Figure 6 that phase-lag-free LPF can compensate the phase lag effectively. Calculate the RMSE between the signal processed by proposed method and the reference signal according to Equation (27). Compared with the original signal, the RMSE of signal processed by phase-lag-free LPF decreased 98.81%.  
Figure 6: Simulation results of signal process based on Kalman filter. The first subfigure is the simulated original-gyro signal, the second subfigure is the signal processed by DFBGA, the third subfigure is the signal processed by DGBGA and SLAIM.  
Figure 7: Simulation results of signal process based on phase-lag-free LPF. The first subfigure is the original signal with phase lag, the second subfigure is the signal processed by phase-lag-free LPF.