esa-sceva/satcom-chunk-collection huggingface dataset:
# A Virtual Network Resource Allocation Framework Based on SR-IOV
## 4 Basic Idea and Implementation
### VF Bandwidth Division  
To limit the network rate of VF, modern SR-IOV network cards (such as Intel 82599 network cards) have added the ability to control the transmission rate on their hardware. They can set a maximum transmission rate independently for each VF without affecting the transmission rate of other VFs or PFs. However, VF has no permission to set the transmission rate itself and can only be set and modified by PF with global operation permission. SR-IOV network card provides Transmit Rate Scheduler (TRS) function to limit the number of each transmission queue in order to limit the transmission rate of VF.  
As shown in Figure 6, it is the flow chart of the SR-IOV network card receiving packets. When a network packet is received by a network card, it is placed in the receiving queue of the corresponding VM by the Layer 2 Sorter according to the MAC address and VLAN of the packet, and then the DMA maps the target DMA address (i.e., the memory address of the VM) to the physical host address. Once the DMA operation is completed, the data will be transferred to the memory address of the VM. At this time, the network card will trigger the interrupt to inform the VMM that the packet has arrived, and the VMM will tell the specific VM to read the data from the transmission queue according to the interrupt vector table to complete the operation of receiving the packet. The process of sending network data by SR-IOV network card is similar to that of receiving. The receiving/sending queue of VF will directly affect the transmission rate of the VM. Therefore, the transmission rate of VF can be limited by limiting the receiving/sending queue.  
To allocate the corresponding size of bandwidth according to the network requirements of the network-intensive VM, we use the method of hierarchical division when limiting the transmission rate of the VF, that is, the maximum bandwidth of each VF is divided according to the step-up method:  
\[\left\{\begin{aligned} VF_{n}=VF_{0}+(n-1)\times\delta\\ \sum_{i=0}^{0}VF_{i}\leq B\end{aligned}\right. \tag{7}\]  
In (7), \(VF_{i}\) is the maximum bandwidth of the i-th VF, \(\delta\) is the size of the step, and \(B\) is the total bandwidth of the network card. The size of \(\delta\) should be set according to the size of the network difference between the data center VM. If \(\delta\) setting is too small, the network-intensive VM with high bandwidth requirements cannot obtain sufficient bandwidth to affect the performance of the system. If it is too large, the VM will have a surplus when using the bandwidth, so that the system cannot achieve the maximum network throughput.
## 6 Performance Results
### Video Latency
This can be attributed to the nature of the encoding algorithms, i.e., the amount of data which needs to be encrypted and transferred in MJPEG compared to H264. The worst option to use when encryption is enabled is Case 4. This conclusion is made based on the difference in video latency values of tests conducted with and without encryption.
## 4 Influence of Axial Joint Transfixion Degree on Energy Dissipation
### Analysis of the Relationship between Energy Absorption and Porosity Change
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Group** & \(V_{\rm S}\) **(cm\({}^{3}\))** & \(T_{\rm R}\) **(ms)** & \(E_{\rm VT}\) & \(\overline{E}_{\rm VT}\) & \(K\) **(\%)** & \(K^{\prime}\) **(\%)** & \(\Delta\)**K **(\%)** \\ \hline \multirow{4}{*}{0\%} & & 0.297 & 0.6993 & & & 10.4021 & 0.0548 \\  & & 0.272 & 0.8736 & & & 10.0370 & 0.3651 \\  & & 0.275 & 0.9079 & 0.9060 & 10.3473 & 12.3047 & 2.2677 \\  & & 0.291 & 1.1704 & & & 14.3392 & 2.0345 \\  & & 0.315 & 1.0314 & & & 9.3151 & 0.4280 \\ \hline \multirow{4}{*}{20\%} & & 0.295 & 0.6623 & & & 9.7563 & 0.4412 \\  & & 0.259 & 0.6623 & & & 12.4683 & 2.7120 \\  & & 0.315 & 0.7284 & & & & 9.8134 & 0.2069 \\ \hline \multirow{4}{*}{40\%} & & 0.292 & 0.5043 & & & 10.0203 & 0.1554 \\  & & 0.268 & 1.1548 & & & 9.8134 & 0.2069 \\  & & 0.266 & 0.5050 & & & 10.6896 & 0.8762 \\  & & 0.257 & 0.7108 & & & & \\  & & 0.281 & 0.5330 & & & 10.1757 & \\  & & 0.286 & 0.8151 & & & & \\  & & 0.288 & 0.9343 & & & 13.0003 & 2.3107 \\  & & 0.363 & 0.7116 & & & & \\  & & 0.324 & 0.7594 & & & & \\ \hline \multirow{4}{*}{60\%} & & 0.290 & 0.6257 & & & 10.9854 & 1.3038 \\  & & 0.265 & 1.1464 & & & 11.3618 & 0.3764 \\  & & 0.275 & 1.3276 & & & & \\  & & 0.291 & 0.5968 & & & 12.4131 & 1.0513 \\  & & 0.299 & 0.4858 & & & & \\  & & 0.318 & 1.0853 & & & & \\  & & 0.313 & 0.9846 & & & 13.5635 & 1.1504 \\  & & 0.365 & 0.6571 & & & & \\ \hline \multirow{4}{*}{80\%} & & 0.275 & 0.6689 & & & 10.7645 & 0.0398 \\  & & 0.291 & 1.0273 & & & 11.1550 & 0.3905 \\  & & 0.285 & 0.6029 & & & 12.4871 & 1.3321 \\  & & 0.277 & 0.4125 & & & & \\ \hline \multirow{4}{*}{100\%} & & 0.308 & 0.7901 & & & 11.6843 & 0.3310 \\  & & 0.283 & 0.5554 & & & 12.3710 & 0.6867 \\ \cline{1-1}  & & 0.291 & 1.2294 & & & 14.1013 & 1.7303 \\ \cline{1-1}  & & 0.292 & 1.1060 & & & & 15.8965 & 1.7952 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: \(E_{\rm VT}\) and pore change data of different transfixion degree joint specimens. saturated water areas. In the case of sufficient geological data, the direction of load action and the length of the transfixion joint can be considered in the design to enhance the load-bearing capacity. Under the action of 0.11 MPa impact air pressure, the average change rate of porosity of the specimen first decreased and then increased with the increase of joint transfixion degree. The average change rates of porosity of the 0-100% transfixion degree specimens are 1.1805, 1.1937, 0.8873, 0.9705, 0.5875, and 1.1358, respectively. It can be seen that the joint transfixion degree has a greater impact on the damage of the specimen when the impact energy is not much different. The porosity variation rates of 0%, 20%, and 100% of the transfixion degree are larger, and the porosity variation rates of 40%, 60%, and 80% are smaller. It shows that the damage degree of the specimen is related to the joint transfixion degree and the ability of the specimen to bear the load due to the freezing of saturated water.
# Future Directions for Electric Propulsion Research
## 4 Challenges for Electric Propulsion Development
### Low-Power Thrusters
#### 4.1.5 Pulsed Plasma Thrusters  
Pulsed plasma thrusters (PPTs) are a simple form of electric propulsion that has been studied extensively theoretically and experimentally, leading to it presently being high on its s-curve in Figure 3. These devices strike an arc across a (typically) solid fuel and then accelerate the resulting plasma along parallel electrodes via the Lorentz force. This is accomplished in a straightforward manner by periodically charging and discharging a capacitor, usually with an igniter circuit involved to trigger the discharge.  
Many of these devices have been developed, such as those by Austrian Research Centers GmbH [42], Mars Space Ltd. [43], Fotec GmbH [44], and Busek [45], and several have flown, starting with the Soviet Zond-2 mission [46] and more recently including Busek's uPPT on Falcon-Sat 3 [47] and several thrusters developed by Nanyang University and Kyushu Institute of Technology [48]. Yet, they are challenged still by the theoretical scaling limits to very low power and the lifetime of these devices, which necessarily strike high-current ablative arcs between electrodes.
## 2 Materials and Methods
### MAGDA Demonstrator Sites
#### 2.1.2 France-Vineyards  
The second demonstrator site is located in Burgundy, France, particularly in the Beaune Valley, a globally renowned wine-producing region. The test site includes vineyards owned by Maison Louis Jadot, a prestigious winery producing high-quality wines. Burgundy's vineyards are highly sensitive to temperature fluctuations, frost events, drought stress, and extreme rainfall, all of which can impact grape quality and yield.  
MAGDA's deployment in Burgundy focuses on climate adaptation for viticulture, using an array of GNSS sensors, Meteorones, and in situ weather monitoring stations installed across different vineyard locations. These sensors track air and soil temperature, humidity, wind speed, and solar radiation, helping viticulturists anticipate weather-related risks and optimize vineyard management strategies.  
MAGDA's implementation in Burgundy is designed to enhance extreme weather anticipation, particularly for convective storms and hail events, although the countermeasures currently available to vineyard managers remain only partially effective. Among the strategies currently in use at Maison Louis Jadot and its network of vineyard growers are field candles and heating wires for late frost, as well as chimney networks for hail protection.  
Another key priority for stakeholders is improving the usability of meteorological data for decision-making. While growers currently rely on an existing forecasting portal,  
Figure 1: MAGDA project sites location and crop types with an example of the deployed sensors.  
they find the data difficult to interpret and not always reliable. There is strong interest in more actionable and accessible weather insights, which aligns closely with MAGDA's interoperability and dashboard tasks.  
Although vineyards in Burgundy do not require irrigation infrastructure, there is a strong demand for better water status monitoring at the vineyard level. Understanding soil moisture conditions can inform adaptive soil management practices, helping growers optimize grape production. The transition from macro-level weather insights to localized, field-specific data is a critical step in improving climate resilience in viticulture, and MAGDA's sensor network is designed to support this shift.
## 4 Simulation Setup
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Description** & **Value** \\ \hline \(m_{B}\) & Mass of the UAV & 4.2 kg \\ \hline \(m_{i}\) & Link mass & 0.3 kg \\ \hline \(I_{B}\) & Moment of inertia of the UAV & 0.4097 kg m\({}^{2}\) \\ \hline \(l_{\text{G}_{i}}\) & Moment of inertia of link \(i\) & 0.0016 kg m\({}^{2}\) \\ \hline \(l_{i}\) & Link \(i\)  length & 0.25 m \\ \hline \(a_{i}\) & Distance from joint \(i\) to link \(i+1\) CoG & 0.125 m \\ \hline \(x_{o}\) & Distance from the UAV CoG to manipulator joint 1 along the UAV body \(x\)-axis & 0 m \\ \hline \(z_{o}\) & Distance from the UAV CoG to manipulator joint 1 along the UAV body \(z\)-axis & \(-0.1\) m \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Geometrical and inertial characteristics of the UAM used in the simulation experiments. Figure 4: **(left)** Curvilinear abscissa, \(s(t)\), of end-effector desired trajectory as a function of time; **(right)** UAM initial configuration in the x–z plane. The green circle represents the UAV CoG, and the blue star represents the whole system CoG, \(G_{UAM}\). In cyan, the desired circular trajectory to be tracked by the end-effector is shown. In order to simulate a realistic scenario in the simulator, three sources of disturbance are modeled:  
* Aerodynamic disturbances;
* Sensors noise in the measurements of the UAV linear and angular positions and velocities used in the inverse kinematics;
* Incorrect inertial parameters in the model used for the computation of the inverse kinematics.
## 3 Problem Formulation
### Objective Functions
#### 3.2.3 Maximization of the Loadability Factor  
The injected active power (\(P_{i}\)) and reactive power (\(Q_{i}\)) at the \(i^{th}\) bus can be expressed in terms of the voltage (\(V\)), the elements of the \(Y_{bus}\) matrix of the system, and the loadability factor (\(\Gamma\)) as follows [32]:  
\[P_{i}=P_{Gi}-(1+\Gamma)P_{Di}-\text{Re}\{V_{i}\sum_{j=1}^{N_{B}}\Bigl{(}V_{j }Y_{i,j}\Bigr{)}^{*}\} \tag{23}\]  
\[Q_{i}=Q_{Gi}-(1+\Gamma)Q_{Di}-\text{Im}\{V_{i}\sum_{j=1}^{N_{B}}\Bigl{(}V_{j }Y_{i,j}\Bigr{)}^{*}\} \tag{24}\]  
where \(P_{Gi}\) and \(Q_{Gi}\) are the active and reactive power generation at the \(i^{th}\) bus, respectively, \(P_{Di}\) and \(Q_{Di}\) represent the base-case active and reactive power consumption at the \(i^{th}\) bus, respectively, and \(N_{B}\) denotes the total number of buses.  
Maximizing the loadability factor is defined as the third objective function, in this paper. However, considering the random nature of the problem, a probabilistic formulation is required. Therefore, by re-formulating Equations (23) and (24), a stochastic formula is derived to obtain the expected loadability factor, as shown in Equations (25) and (26).  
\[P_{i,s}=P_{Gi,s}-(1+\Gamma(s))P_{Di,s}-\text{Re}\{V_{i,s}\sum_{j=1}^{N_{B}} \bigl{(}V_{j,s}Y_{i,j,s}\bigr{)}^{*}\},\qquad\forall s\in\Omega_{s} \tag{25}\]\[Q_{i,s}=Q_{Gi,s}-(1+\Gamma(s))Q_{Di,s}-\text{Im}\{V_{i,s}\sum_{j=1}^{N_{B}}\bigl{(} V_{j,s}Y_{i,j,s}\bigr{)}^{*}\},\qquad\forall s\in\Omega_{s} \tag{26}\]  
where \(P_{i,s}\) and \(Q_{i,s}\) denote the injected active and reactive power at the \(i^{th}\) bus for the \(s^{th}\) scenario, respectively, \(P_{Gi,s}\) and \(Q_{Gi,s}\) represent the active and reactive power generation at the \(i^{th}\) bus for the \(s^{th}\) scenario, respectively, \(P_{Di,s}\) and \(Q_{Di,s}\) are the base-case active and reactive power consumption at the \(i^{th}\) bus for the \(s^{th}\) scenario, respectively, \(\Gamma(s)\) denotes the loadability factor for the \(s^{th}\) scenario; \(V_{i,s}\) and \(V_{j,s}\) indicate the voltage of the \(i^{th}\) bus \(j^{th}\) bus for the \(s^{th}\) scenario, respectively, and lastly, the elements of the \(Y_{bus}\) matrix for the \(s^{th}\) scenario are shown by \(Y_{i,j,s}\).  
According to the above-mentioned descriptions, the third objective function (\(f_{3}\)), which is the expected value of the loadability factor, can be derived as follows:  
\[f_{3}=\sum_{s\in\Omega_{s}}\pi_{s}\Gamma(s) \tag{27}\]  
Finally, the optimization criteria subjected to equality and inequality constraints are as follows:  
\[\text{Optimization Criteria}=\left\{\begin{array}{l}\min(f_{1})\\ \min(f_{2})\\ \max(f_{3})\end{array}\right. \tag{28}\]
# A Sensor Drift Compensation Method with a Masked Autoencoder Module
## 3 Experiment and Results
### Experiment Setting
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{3}{c}{**Number of Parameters**} & & \\ \cline{2-6}  & **Ethanol** & **Ethylene** & **Ammonia** & **Acetaldehyde** & **Acetone** & **Toluene** \\ \hline MLP-normal & 18,465 & 18,465 & 18,465 & 18,465 & 18,465 & 18,465 \\ MLP-PLS & 19,425 & 19,457 & 18,689 & 19,489 & 19,361 & 20,353 \\ MLP-encoder (\(n=2\)) & 18,747 & 18,747 & 18,747 & 18,747 & 18,747 & 18,747 \\ MLP-encoder (\(n=3\)) & 18,788 & 18,788 & 18,788 & 18,788 & 18,788 & 18,788 \\ MLP-encoder (\(n=4\)) & 18,829 & 18,829 & 18,829 & 18,829 & 18,829 & 18,829 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The number of parameters of models, where \(n\) denotes for latent space dimension of the autoencoder model.
# Fusion Network for Change Detection of High-Resolution Panchromatic Imagery
## 2 Deep Convolutional Network and Related Studies on Change Detection
Alternative algorithms for change detection were introduced by employing a high-level differential feature with a dual-path network, as shown in Figure 2b. Siamese CNN (SCNN) was proposed to detect changed areas for multimodal remote sensing data [27]. This architecture was employed to learn the different characteristics between multimodal remote sensing data. This approach learns the feature map of temporal images in each path network. The Euclidean distance was employed to measure the similarity at the back-end of the network. A similar method was developed based on an SCNN for optical aerial images [25]. A deep CNN was proposed by producing a change detection map directly from two images [33]. A change map was evaluated using the pixel-wise Euclidean distance from high-dimensional feature maps. Another method was proposed that incorporates a deep stacked denoising autoencoder (SDAE) and feature change analysis (FCA) for multi-spatial resolution change detection [34]. In the aforementioned study, denoising autoencoders were stacked to  
Figure 2: Front-end differential network (FDN) and back-end differential network (BDN) architectures: (**a**) Difference image (DI)/Joint features (JF) + single CNN, and (**b**) Dual-CNN. learn local and high-level features for unsupervised learning. Then, the inner relationship between the multi-resolution image pair was exploited by building a mapping neural network to identify any change representations. A dual-dense convolutional network was presented by incorporating information from neighbor pixels [28]. In the aforementioned study, a dense connection was used to enhance the features of the changed map information. All of the above-mentioned BDN architectures yield good performances by inspecting high-level differential features, which can reduce false positives. However, a BDN can achieve higher sensitivity and specificity through high-level differential features. Although a high-level differential network can improve the sensitivity and specificity, the false negative rate is still too high for practical applications. The FDN architecture can achieve a relatively higher true-positive rate regardless of the number of false positives. In addition, the BDN architecture can reduce the false-positive rate by producing some false negatives caused by strict decision criteria in high-level differential features. In this work, an FDN and a BDN were fused to employ the advantages of both. A post-processing step was then employed during the inference stage to obtain the final decision for change detection.
# The Impact of the S-Adenosylmethionine Analogue Sinefungin on Viral Life Cycles
## 3 Results  
### Cell Viability  
To evaluate the cytotoxicity of sinefungin, VERO-76 cells were selected and also used for the antiviral tests (Figure 1). The cells were exposed to sinefungin at concentrations ranging from 12.5 to 200 \(\upmu\)g/mL for 24 h. The cytotoxic effect was evaluated relative to CTRL-. No concentration tested induced complete cytotoxicity. Sinefungin reduced the cell viability by approximately 21.7% at 200 \(\upmu\)g/mL, 16.6% at 100 \(\upmu\)g/mL and 8.8% at 50 \(\upmu\)g/mL. Lower concentrations (25 and 12.5 \(\upmu\)g/mL) showed negligible effects, with reductions in viability below 5%. CC\({}_{50}\), determined using the non-linear regression analysis, was greater than the highest concentration tested (CC\({}_{50}\)\(>\) 200 \(\upmu\)g/mL).
## 2 Materials and Methods  
### Materials  
Determination of melting points was conducted using a Gallenkamp Melting Point Apparatus Aar 3235 (Gallenkamp, Cambridge, UK). Infrared spectra (IR) were measured by IRTracer-100 (Shimazu, Japan). 1H nuclear magnetic resonance spectrometry (1H-NMR) was implemented on a Bruker AV-500 NMR Spectrometer (Bruker, Germany). Mass spectrometry (MS) was measured on a Waters Quattro Micro LC/MS-MS System (Waters Alliance, MA, USA). Anthranilic acid, acetic anhydride, acetic acid, polyphosphoric acid, N-bromosuccinimide (NBS), pyridine, chloroform, sodium sulfate, silica gel for column, thin-layer chromatography (TLC) (silica gel 60 F254), imidazole, triazole, 2-aminothiazol, ethanol, metal sodium, chloroform, potassium carbonate, and dimethyl sulfoxide (DMSO) were purchased from Sigma-Aldrich (Burlington, MA, USA). _C. albicans_ ATCC 10231, _A. niger_ ATCC 16404, _E. coli_ ATCC 25923, _P. aeruginosa_ ATCC 27853, _B. subtilis_ PY 79, _S. faecalis_ ATCC 29212, _S. aureus_ ATCC 25922, and methicillin-resistant _S. aureus_ (MRSA) ATCC 43300 were obtained from American Type Culture Collection (Manassas, VA 20110, USA).
## 3 Results and Discussions  
### Measured Porosity and Effective Porosity of Pervious Concrete  
The values of properties of pervious concrete are the average values of the three specimens. The error bar is the standard deviation of the values of the three specimens. The shorter the error bar is, the smaller the experimental error is, and the more accurate the results are. The effective porosity and measured porosity of the pervious concrete under different target porosities are represented in Figure 5. As shown in Figure 5, the measured porosity of the pervious concrete ranged from 7.05% to 29.32%, which is slightly more than the effective porosity ranging from 5.12% to 27.14% when the target porosity of pervious concrete was designed from 8% to 30%. These differences indicated that part of the designed pores were compacted with hydration products, called 'compacted pores', or surrounded and semisurrounded by hydration products, called closed or semiclosed pores. Figure 6 presents the schematic of the component and structure of the pervious concrete. Pervious concrete is composed of hydration products, iron tailings, compacted pores, closed pores, and open pores. The number of open pores is the controlling factor of the water permeability of pervious concrete. The water on the upper surface of pervious concrete can only flow through the open pores to the lower surface of pervious concrete.  
Figure 4: Constant falling-head permeability setup.  
concrete but cannot flow through the closed pores or semiclosed pores, which can be measured by Image-Pro Plus. Apart from the test error, this is the main reason for the difference between the effective porosity and measured porosity of the pervious concrete. Although there is a certain gap between the effective porosity and the target porosity, within a certain correction range, it shows that the design formula of the pervious concrete in this paper is reasonable and can, to a certain extent, be used as a reference. Although there is a gap between the measured porosity and target porosity, ranging from 1.2% to 2.4%, it only accounts for 5% to 10% of the target porosity and these values were within the design error margin. Therefore, the design method of pervious concrete used here was, to a certain extent, reasonable and was feasible for pervious concrete using iron tailings as coarse aggregates.  
Figure 5: Effective porosity and measured porosity of the pervious concrete with different target porosities.  
Figure 6: Schematic of the component and structure of pervious concrete.
# A Query Understanding Framework for Earth Data Discovery
## 4 Methodology  
### Query Understanding Framework Architecture  
The conceptual query understanding framework has four components: spatial and temporal parsing; concept recognition; name entity recognition; and, semantic query expansion (Figure 3). Spatial and temporal parsing extracts spatial bounding box and temporal range from a query. Concept recognition isolates domain phrases from the query, and name entity recognition focuses on identifying entities, like people, place, and organization within a query. Finally, query expansion adds additional phrases (e.g., synonyms) to expand the original query. For a given query, the spatial bounding box (coordinates of northeast and southwest points of the spatial range) and time range are detected, and a query remainder is sent to the concept recognition module for domain phrase detection. For example, \"pacific ocean\" and \"2003-2010\" in the input query \"sea surface temperature level 3 pacific ocean 2003-2010\" are identified as spatial bounding box and time range, while \"sea surface temperature\" and \"level 3\" are domain phrases. The name entity recognition module recognizes \"sea surface temperature\" and \"level 3\" as a variable and processing level. The query expansion module augments extracted phrases with their synonyms and acronyms learned from Web usage logs and metadata. For example, \"sea surface temperature\" in the query is converted to \"sea surface temperature OR sst\". Once all of the spatial-temporal features, domain phrases, entities, synonyms, and acronyms are detected, the original query is converted to a new one, and the top \(k\) results are retrieved by the search engine while using the rewritten query.  
Figure 2: Sample Physical Oceanography Distributed Active Archive Center (PO.DAAC) HTTP logs in Combined Log Format.