esa-sceva/satcom-chunk-collection huggingface dataset:
# TRAP: A Three-Way Handshake Server for TCP Connection Establishment
## 3 Related Work
### SYN Cookie  
In order to prevent a SYN flood attack from exhausting all of a server's resources, when receiving a SYN packet, the client information required for future connection like the source IP address and the TCP port number are encoded directly into the replying SYN-ACK packet's initial sequence number (ISN).  
When a client replies with an ACK packet, the packet's acknowledge number are extracted, and compared with a newly-generated hash based on the client's information to verify if the packet is a continuation from a previous connection and a previous verified host. In addition to the client information, a time-window value is also checked to verify the integrity of the cookie.  
A server adopting SYN cookies does not save any state of the client in the memory prior to its establishment, thus will not be facing resource outages when a SYN flood occurs. Figure 5 shows a simplified illustration of the procedure of SYN cookies.  
However, as the TCP protocol specific requires the retransmission of unacknowledged data, after applying SYN cookies, this will be impossible since no state of a client is kept on the server [15]. This may also prevent a legitimate client from connecting to the server.  
The hash generation process may also cost many CPU cycles under heavy network load, defecting its purpose of mitigating a DDoS attack.
## 5 Results and Discussions
### Pre-Treatment, Co-Digestion, and Biogas Upgrade  
Biomethane yields from sludge AD are low when compared to other types of biomass, due to the presence of complex organic structures, microbial flocs (activated sludge), and other inhibitory compounds. However, the pre-treatment of sludge prior to AD can improve biodegradability of sludge and increases CH\({}_{4}\) production [28,57]. Further improvements can be achieved through co-digestion of sludge to adjust moisture content, C/N ratio and nutrient balance [28]. Co-digestion of sludge with lipids, such as fatty wastewater, meat processing by-products, food waste and organic municipal solid waste, can increase CH\({}_{4}\) production [28]. In addition to enhanced biomethane yield, co-digestion allows the cost sharing of various waste streams in a single digest. However, the main drawbacks of co-digestion are the transportation costs and feedstock inconsistencies. Biogas can be valorized further by transforming it into enriched biomethane (natural gas), which has a higher market value and can be used by power plants, industry and households. Typically, the composition of biogas is 50-75% CH\({}_{4}\), 25-50% CO\({}_{2}\) and other impurities. The utilization of raw biogas as a direct natural gas substitute (up to 95% CH\({}_{4}\)) is limited due to the potential corrosion of pipelines in the gas grid and inconsistencies in the calorific value of biogas. Thus, the purification and upgrading of biogas to biomethane are necessary for the removal of CO\({}_{2}\) and other impurities. Purification and upgrading technologies are based on adsorption and absorption principles, or separation by membranes [5,58,59].
## 6 Simulation-Based Implementation, Experimental Results and Discussion  
In the simulation, the network model was based on IEEE 802.15.4 MAC and physical layers; the network parameters that were used to configure the WSN scenario in ns-2 are given in Table 3. In all simulations, a many-to-one traffic pattern was utilized, and after every 180 s, the routing table record was deleted to keep the neighboring information updated. Similar to the topology used by [PERSON] and [PERSON] and [PERSON] (2016), the simulation topology consists of 121 nodes distributed in an 80 m \(\times\) 80 m region, as shown in Figure 5. The nodes numbered 120, 110, 100 and 90 are the source nodes, node 0 is the sink node, and nodes 24, 25, 31 and 36 are the adversary nodes. The performance of E-BIOSARP was evaluated in terms of energy consumption, data packet overhead, and packet delivery ratio.
## 2 Materials and Methods
### Temperature Loss Modelling  
Module temperature may significantly affect PV systems' efficiency, especially BIPV ones due to the more limited ventilation of the modules. Temperature loss is directly proportional to the cell's temperature increase, with a higher temperature difference resulting in higher losses and, therefore, lower PV output power (Equation (4)):  
\[L_{\text{temp}}=\gamma\cdot\Delta T_{\text{c}}=\gamma\cdot(T_{\text{c}}-T_{ \text{c,0}}) \tag{4}\]  
where \(\gamma\) (\(\%/^{\circ}\)C) is the PV technology-dependent temperature power loss coefficient and \(\Delta T_{\text{c}}\) is the cell temperature difference between operating (\(T_{\text{c}}\)) and reference (\(T_{\text{c,0}}=25~{}^{\circ}\)C) conditions. The IEC 60891-3 Standard [58] indicates how to determine \(\gamma\) experimentally, while the IEC 61853-2 [59] includes the procedure to determine the nominal module operating temperature (_NMOT_).  
Calculating temperature losses requires knowing the cell temperature \(T_{\text{c}}\) (usually inferred from the module temperature), as seen in Equation (4). While this can be measured in PV installations to account for losses under real operating conditions, when designing a PV system module, temperature needs to be calculated based on one of the different available models. The PV sector has developed different analytical expressions over the years to determine cell temperature, usually assuming steady-state conditions. Temperature models have been mainly applied to modules in ground-level PV plants, where their boundary conditions, such as ventilation, stand-off, and position (tilt angle), differ from those in buildings. As PV in buildings progressively gains weight in the global PV market and BIPV designs become more sophisticated, efforts are being made to adapt these existing _NOCT_ models to better describe the temperature of BIPV modules.  
Five temperature models commonly used by the industry were chosen in this study, Ross, King, Faiman, PVsyst, and SAPM, as well as a novel approach proposed by [PERSON] [60]. PV cell and module temperatures were calculated with each temperature model's formula and parameters and compared with experimental temperature data through statistical indicators to assess the suitability of each model. The variables used in the temperature models are cell temperature (\(T_{\text{c}}\)), module temperature (\(T_{\text{m}}\)), ambient temperature (\(T_{\text{a}}\)), plane-of-array irradiance (\(G_{\text{POA}}\)), and wind speed (\(W\)s) measured at a standard height of 10 m.
# Retrofitting of a Multi-Span Simply Supported Bridge into a Semi-Integral Bridge
## 5 Finite Element Analysis
### Retrofitting Calculation  
5.1.1. Beam Internal Forces  
The models were analyzed for both the original simply supported bridge and the transformed semi-integral bridge due to dead load, vehicular live load, concrete creep, and shrinkage in combination with thermal loads. The results are summarized in Table 1.  
Compared with the simply supported bridge, the maximum positive moment decreases in the transformed semi-integral bridge, and a negative moment appears at interior support with a value of 423.8 kN\(\cdot\)m. The maximum positive moments at the midspan decrease by 11.1% under a combined load. Shear forces in the main beam decrease slightly at the abutments after the transformed semi-integral bridge, but shear forces increase at the interior pier by 6.4% under a combined load.  
Additionally, the simply supported beams exhibit no axial force under combined loading conditions. Nevertheless, after transforming the simply supported bridges into semi-integral bridges, the most unfavorable axial force at the beam end reaches 219.2 kN under combined loading conditions.
## 4 Results
### FPCS of the Sentinel-2 MSI Data  
PC4 of the raw Sentinel-2 data had the maximum negative loadings of (\(-0.644\)) and (\(-0.329\)) from the SWIR1 and SWIR2 bands showing granite in medium-toned pixels due to the absorption of the associated muscovite in both bands. It had the maximum positive loadings (\(0.492\)) from the NIR band and a negative (\(-0.122\)) red band corresponding to vegetation's reflection and absorption features in these bands. PC3 of the stretched data has negative loadings of \(-0.333\) and \(-0.380\) from the SWIR1 and SWIR2 bands, respectively, due to the absorption of calcite and reflection in RGB bands. PC3 of the raw data had positive loadings of \(0.278\) and \(0.257\) for the blue and SWIR1 bands, respectively, indicating the reflection features of quartz and orthoclase; however, it is indicated as medium-toned darker pixels due to epidote and muscovite absorption in the SWIR2 band (\(-0.607\)).
## 2 Hybrid Real-Time Scheduler in the MIROS
### Performance Evaluation
#### 2.6.2 Data Memory Consumption of the Different OS Schedulers  
The data memory consumption of the event-driven scheduler can be evaluated as follows:  
\[E_{I}=Size(DATA/BSS)+Size(PCB)+Size(SQ)=M_{DATA/BSS}+S_{PCB}*N_{PCB}+S_{SQ}*L_{SQ} \tag{1}\]  
in which \(M_{DATA/BSS}\) represents the size of the DATA/BSS sections (the global variables, static variables, _etc._). \(S_{PCB}\) and \(N_{PCB}\) represent respectively the structure size of the _process control block_ (in SOS, it is named as _module control block_) and the number of the defined _processes_. \(S_{SQ}\) and \(L_{SQ}\) represent respectively the size of each entry in the scheduling queue (SQ) and the queue length of the SQ.  
The data memory consumption of the multithreaded scheduler can be evaluated as follows:  
\[E_{2}=Size(DATA/BSS)+Size(TCB)+Size(STK)=M_{DATA/BSS}+S_{TCB}*N_{TCB}+S_{STK}*N_{ THRD} \tag{2}\]  
in which the \(S_{TCB}\), \(N_{TCB}\), \(S_{STK}\) and \(N_{THRD}\) represent respectively the structure size of the _thread control block_ (TCB), the number of the thread TCBs, the size of the thread stack and the number of the allocated threads.  
In Table 1, the date memory consumption of the different schedulers is listed. In the _MIROS_, the flag-polling mechanism is used for the event-driven scheduling, thus no scheduling queue exists. In _TinyOS_, _Contiki_ and _MIROS_, the multithreading is used either only for the application (TinyOS), or only for the system preemption-needed _processes_ (Contiki), or dedicated to the real-time tasks (MIROS). Therefore, the number of the threads and the TCB in these OSes is smaller if compared to those in the pure multithreaded OS mantisOS. Provided by a scenario shown in Table 1, the data memory size of the different OS schedulers can be computed.  
Figure 5: Code size of the event-driven and multithreaded schedulers in the different OSes (bytes).  
From the results in Table 1, it can be concluded that the MIROS hybrid scheduler can support real-time scheduling by consuming less data memory resources (compared to the pure multithreaded OS mantisOS). Although the data memory costs of the TinyOS TOSTThread and the Contiki multithreading are also not high, the real-time scheduling cannot be guaranteed in these systems (Section 2.5).  
From the results in Figure 5 and Table 1, it can be concluded that the code memory size and the data memory size of the SOS scheduler is larger if compared with those of the other event-driven OSes; this is because SOS is a dynamic module-based OS. In SOS, the complicated module management and indirect access mechanisms need to be implemented.
# Electrospinning-Based Carbon Nanofibers for Energy and Sensor Applications
## 3 Different Carbon Nanofibers Obtained by Electrospinning
### Carbon Nanofibers from Modified Dual-Nozzle Electrospinning  
To manually create multilayer fibers using an electrospinning system, the engineers created the dual-nozzle needle (Figure 10). The structure of a dual nozzle includes a larger-sized cover and a smaller-sized nozzle, which will be fed with two different solutions. Using the dual nozzle, the user can easily control the position of the polymer layers in the product fibers. Despite the advantages, the use of the dual nozzle requires good control of the two solutions, from the concentration to the flow rate.  
Figure 9: (**a**) Fabrication of multichannel CNFs decorated with Ru nanoparticles [76]. (**b**) Fabrication of CNFs decorated with ZnO/SnO\({}_{2}\)[79]. (**c**) Fabrication of CNFs decorated with MOF material [80].  
[PERSON] et al. used the dual nozzle to fabricate porous, hollow CNFs with gold nanoparticles decorated on the inside (Figure 11a) [81]. Although the inner nozzle was fed with a poly(styrene-co-acrylonitrile) solution containing gold chloride (AgCl\({}_{3}\)), the outer nozzle was fed with a PAN solution mixed with poly(styrene-co-acrylonitrile). The dual nozzle allows the copolymer to stay inside and create a copolymer core for the electrospun nanofibers. After carbonization, the copolymer is decomposed and the gold nanoparticles remain attached to the carbon shell. The hollow fibers combined with catalytic magne-siophilic provided excellent magnesium (Mg) plating/stripping in metallic Mg battery applications.  
Figure 11: (**a**) Fabrication of hollow porous CNFs decorated with gold nanoparticles on the inner wall [81]. (**b**) Fabrication of TiO\({}_{2}\) nanofibers and TiO\({}_{2}\) hollow nanofibers [82]. (**c**) Fabrication of hollow CNFs containing CuCo\({}_{2}\)O\({}_{4}\) nanoparticles [83].  
Figure 10: Structure of dual nozzle for electrospinning.  
[PERSON] et al. introduced the nitridated hollow titanium dioxide (TiO\({}_{2}\)) fibers, which act as an anode for lithium-ion batteries (Figure 11b) [82]. Although the inner nozzle was fed with heavy-mineral oil, which later decomposed during carbonization, the material for the outer nozzle was a mixture of polyvinylpyrrolidone (PVP) solution and titanium tetraisopropoxide (Ti(OCH(CH\({}_{3}\))\({}_{2}\))\({}_{4}\)). After the carbonization process, the oil and PVP decomposed leaving only TiO\({}_{2}\). Due to the larger surface area, the hollow CNFs showed higher working capacity compared to nanometer-sized titanium oxide.  
[PERSON] et al. fabricated thin-wall hollow CNFs with dual-nozzle electrospinning (Figure 11c) [83]. Since low molecular-weight PAN has a lower decomposition temperature, it evaporated and the carbon layer was formed from the high molecular-weight PAN precursor. CuCo\({}_{2}\)O\({}_{4}\) nanoparticles were also present inside the carbon layer that was made of copper nitrate and cobalt acetate mixed into the high molecular PAN solution.
## 2 Materials and Methods
### Module 4: Multi-Pass Dealiasing  
The dealiasing scheme began with the radial following to the left and right side of the first reference radial selected from module 2 passing through two steps: one step went through 180\({}^{\circ}\) in a clockwise course and the other step went through 180\({}^{\circ}\) in a counterclockwise course.  
Figure 1: (**a**) Raw radar radial velocities of the elevation angle of 6.0\({}^{\circ}\) of Guangxi radar station at 2028 (UTC), 30 March, 2014. (**b**) The number of flags resulting from the procedure of preserving the convection regions at the same elevation angle of the same radar. Flag 1 denotes the boundary of the aliased area. Flag 3 shows the real shear regions.  
The dealiasing scheme executed four steps in the radial and azimuth direction in each half circle. First, dealiasing occurred from the inner to outer area for each radial in the azimuth direction; second, dealiasing from the outer to inner area for each radial in the azimuth direction took place; third, dealiasing from the inner to outer area for all the points with the same distance from the Doppler radar station in the radial direction occurred; and fourth, dealiasing from the outer to inner area for all the points with the same distance from the Doppler radar station in the radial direction took place. The dealiasing scheme performed utilizing a coherence check that contrasted its radial velocity gate with the reference radial velocity information that was chosen from the dealiased velocity gates within the preceding gates for each observation gate. Because of the continuity of the wind speed, the coherence test checked the difference between the current gate and the proximal gates in the preceding correctly dealiased radial. In this study, dealiasing was performed through minimizing the difference between the current gate and the proximal gates. The reader can refer to He2012 for the main details of choosing the reference radial velocity. The two ways and two directions scheme did not need to depend on the radiosonde information or velocity-azimuth display wind profile, and it was powerful for managing the near-range aliased problem and aliased gates near to missing observation.
# GNSS Interference Monitoring and Detection (GIMAD) System +
## 3 GIMAD Prototype Description
### Signal Processing Module (SPM)
#### 3.1.1 Detection Modules/Algorithms  
I-SAM and O-SAM modules from the Detection block in Figure 5 will assess the presence or not of RFI signals, performing a time-frequency characterization when a detection is confirmed.  
The selected algorithms for interference detection in I-SAM/O-SAM are:  
Excess Kurtosis;  
Normalized sixth cumulant;  
Power level;  
Excess Kurtosis Fast Fourier Transform (FFT);  
Sixth cumulant FFT;  
Power Spectral Density (PSD) Peak Power.  
If an interference alert is provided by any of the previous algorithms, the time-frequency characterization of the collected signal is performed.  
Further details on algorithms and decision keys for selection can be found in [1].  
Complementary to previous detection modules, the MAM module will be in charge of monitoring the GNSS receiver post-correlation observables for both detecting the presence of interference in the GNSS band and assessing the impact of the interference signals in the GNSS receiver. This is done by constantly comparing the current time evolution of each monitoring sudden changes in the measured observables.  
The selected parameters for RFI detection and impact assessment in MAM are:  
Automatic Gain Control (AGC) level variations;  
Carrier-to-Noise Ratio (C/N0) variations;  
Pseudorange deviations;  
Carrier phase deviations;  
Position accuracy.
## 4 Settings, Results and Discussion
### Ablation Studies
#### 4.2.1 Architecture Agnostic  
Since LBSD is irrelevant to the model, it can be integrated into various VQA networks. To evaluate the performance of LBSD on debiased methods, the authors of this paper combine it with other typical methods and baseline, including UpDn, Bias product (Product of Experts), reweight and LMH. Reweight, a non-ensemble method, encourages the model to focus on the samples that are predicted erroneously by the language bias model. While Bias product and LMH are ensemble models. Compared with these, the LBSD-integrated models have higher precision.  
The authors of this paper conduct ablation experiments on the VQA-CP v2 and VQA-CP v1 datasets. As shown in Table 2, for typical debiased methods, including ensemble and non-ensemble methods, LBSD improves the precision of the model on the VQA-CP v2 dataset. For example, the performance of reweighting (non-ensemble) and LMH (ensemble) improves by 1.26% and 2.22%, respectively. Even for UpDn without debiased methods, LBSD improves the precision by 0.25%, which demonstrates that LBSD reduced the language bias from the perspective of feature learning. As shown in Table 3, for reweight (non-ensemble) and bias product (ensemble), LBSD improves the performance by 2.2% and 0.58% (\"NUM\" index has been improved by 7.51%), respectively, on the VQA-CP v1 dataset.
## 4 Testbed Development
### Testbed Architecture
#### 4.2.5 Traffic Compression of RFS Simulator  
OAI indicates that RFS simulator requires a high-speed connection to operate optimally due to the transmission of large volumes of I/Q samples. As a consequence of its non-real-time implementation, the absence of a scheduler to distribute these transmissions over time results in the tendency to maximize CPU and network bandwidth consumption. Thus, the first to limit will determine the operational bitrate of RFS simulator.  
In light of the Starlink metrics obtained in Section 4, the less than 14 Mbps result achieved in the uplink with the 1-stream TCP connection poses a substantial bottleneck for RFS simulator. After analyzing the transmitted I/Q samples, we realized that most of them were _null_, proving that there exists a continuous sending of I/Q samples even if the generated signal does not carry data. To optimize this traffic, we opted to apply the fast lossless compression algorithm Zstandard (ZSTD) [56] on I/Q samples, which resulted in a notable reduction in bitrate.  
In order to quantify the compression, we utilized two VMs running on the same host to measure the bitrate and the CPU time consumed by RFS simulator in the UE. Each VM allocates 4 virtual CPU cores and has 4 Gbps of network bandwidth available. The results, depicted in Figure 9, confirm that bitrate now meets the throughput limits of Starlink. Also note the reduction in CPU time, an unexpected behavior which points to the emergence of a new bottleneck in the implementation that prevents the exploitation of the CPU and the bandwidth. This hypothesis is reinforced by the service degradation observed in the UE, since the compression significantly increases RTT when performing a ping to the UPF, as shown in Figure 10. The \"hidden\" bottleneck might be altering the nominal performance of RFS simulator.  
The present traffic compression is only envisaged for the third proposed architecture with the relay of the NR signal. Despite the speed of ZSTD, the persistent compression and decompression of I/Q samples lead to the accumulation of short delays that are eventually materialized in large RTT at the application level (from about 20 ms to the range of 80-100 ms). This huge degradation will strongly affect further results, so it must be considered in future experiments. The inability to utilize the uncompressed version of RFS simulator over Starlink made traffic compression indispensable.
# Hollow-Core Optical Fibers for Telecommunications and Data Transmission
## 13 Associated Technologies  
The wide adoption of hollow-core fibers requires the availability of compatible passive and active devices, and test instruments. The disruptive novelties will likely be the following:  
* Splicing of HCFS, in particular to SMFs--presented in Section 10;
* New transmission bands and optical amplifiers;
* Purging of contaminated fibers.  
Additionally, an HCF-HCF connector for high-power (\(\approx\)50 W) applications, compact, and sealed is desirable, e.g., for DWDM networks with optical amplifiers.