esa-sceva/satcom-chunk-collection huggingface dataset:
# Compressed Nonlinear Equalizers for 112-Gbps Optical Interconnects: Efficiency and Stability
## 3 Experiment and Results
### Stability Comparison  
Performance stability for equalization is of great significance in the real application where output optical power or 3-dB bandwidth of critical devices in the optical interconnect will change within a life-cycle because of component aging or ambient temperature fluctuation. It is likely that performance stability will become worse with pruning algorithms since that the redundancy of connections in the equalization will be useful in changing scenarios. To verify the stability of pruning algorithm with changing link conditions, we choose DC bias of VCSEL as a key parameter for further evaluation because DC bias variation of VCSEL will not only change the output power but also bandwidth of directly modulated laser. The transmission experiment is run with a 100 Gbps PAM4 pre-distorted link. We set \(VE(51,23,11)\) with complexity of 1461 and \(NNE(31,41,4)\) with complexity of 1435, respectively. BER performance under different bias is shown in Figure 7. The BER bathtub curve indicates that equalization performance variation, instead of bandwidth variation with bias current, is the main reason of BER deviation. First, BER is calculated as function of bias for NNE and VE with B2B and 100 m condition. Figure 7a shows stable BER level with bias from 4 mA to 10 mA. 6 mA, the optimal  
Figure 5: The efficiency comparison for (a) B2B case and (b) 100 m MMF case at 100 Gbps PAM-4 with pre-distortion.  
Figure 6: The efficiency comparison at 112 Gbps PAM-4 without pre-distortion.  
operating point of VCSEL, is chosen as a benchmark point. Second, pruning algorithms for VE and NNE are running and optimized at the bias of 6 mA. The same pruned equalizers are then applied to data received with a different bias current for VCSEL. As we can see from Figure 7b, the pruned VE optimized for 6 mA bias also shows excellent tolerance for bias from 7 mA to 9 mA. This means that sparse VE structure has consistent equalization performance within a bias range of 2.5 mA in this experiment. However, this is not case for NNE. Pruned NNE, optimized using data of 6 mA bias, seems less effective and more fluctuating for cases with a different bias. The experimental results in Figure 7b of fluctuation BER with pruned NNE shows that the iterative pruning and retraining approach has broken the equalization resilience due to reduced connections between layers.
# Parametric Analysis of a Polygeneration System with CO\({}_{2}\) Working Fluid
## 1 Introduction
The system is optimized exergo-economically and the exergo-economic factor was found to be 28.23%. In another work, [PERSON] and [PERSON] [21] examined a trigeneration unit with parabolic trough solar collectors, supercritical CO2. Brayton cycle and a bottoming absorption cycle. They found that the optimum maximum temperature occurred at 650 K and in this case, the exergy efficiency was about 75% and the energy efficiency was about 45%. [PERSON] et al. [22] optimized a biomass-driven trigeneration system with a CO2-Brayton cycle and an absorption chiller. They found maximum energy and exergy efficiencies at 43.7% and 47.8%, respectively. Interestingly, [PERSON] et al. [23] studied a trigeneration system with a supercritical CO2 recompression Brayton cycle and a Rankine cycle with an ejector device. This unit was fed by heat from a nuclear reactor and it was optimized with a multi-objective optimization procedure. The exergy efficiency was found to be up to 69% and the trigeneration configuration had 9.2% higher exergy efficiency compared to the system with supercritical CO2, which produced only electricity. Additionally, an interesting optimization was performed by [PERSON] et al. [24] for a CO2-based trigeneration system. They found that the optimum cooling to electricity ratio had to be in the range of 1.37 to 1.53. Lastly, [PERSON] and [PERSON] [25] studied a geothermal-based trigeneration system with CO2. They studied two systems with a modified Brayton cycle with an ejector inside the system. They found that the use of an internal heat exchanger in place of the gas cooler led to enhancement in the performance and to a maximum exergy efficiency of 32%. Finally, the use of the CO2 transcritical cycle for storage issues can be applied in polygeneration systems as an alternative choice as has been suggested by [PERSON] et al. [26]. Our literature review indicates that there is a lot of interest in trigeneration and polygeneration energy systems with renewable energy sources and natural refrigerants. In this direction, the present work investigates a novel polygeneration system for refrigeration, power and heating production at two temperature levels. This system includes a thermodynamic cycle that is practically, a combination of a mechanical compression power cycle and a recompression Brayton cycle. The unit is fed by biomass and the working fluid is CO2. To our knowledge, there are no other studies that have investigated a system such as the present one, which combines four useful products, a renewable energy source as the heat input (biomass in this case) and a natural refrigerant as the only working fluid. Thus, this work suggests a totally new, and source configuration. The system was examined in steady-state conditions and it was analyzed parametrically. The study was conducted by using a developed thermodynamic model in Engineering Equation Solver [27].
## 4 Performance Analysis
### Average Bep/SEP
#### 4.2.1 ABEP/ASEP in the Exponential Form  
From Table 2, we can see the conditional BEP expressions of DPSK are written in the exponential form. For this case, the ABEP expressions for the single-link and MRC systems over independent and correlated fading channels can be derived by using the approaches to find the MGF of \(\gamma\), such as (9). To this end, the exact ABEP expressions for various systems are obtained in Table 3.
## 4 Experimental Results
### Driving Performance Evaluation
#### 4.2.2 Case 2: Lane Changing  
In Figure 9, rapid sequential lane changes are observed. Between 1100 and 1300 m, two-lane changes take place consecutively. The detailed trajectory is depicted in Figure 10, where the proposed method adeptly follows the target lane, while the baseline method experiences delay and failure during the lane change. Additionally, between 1400 and 2200 m, the baseline method fails to maintain the lane, indicating deficiencies in steering control. Furthermore, within the desired speed range of 20 to 30 m/s, TD3 also struggles with velocity control.  
Figure 8: Case study of lane-keeping performance: Section from 1100 m to 1500 m in Scenario 1. (**a**,**b**) depict the lane-keeping maneuvers of our method and the baseline method, respectively. (**c**,**d**) show the steering angles corresponding to the maneuvers in (**a**,**b**). “CoL” denotes the center of the lane, representing the optimal position for the vehicle. The red rectangle in (**a**) and the green rectangle in (**b**) denote the location of the vehicle at each labeled time step. In this lane-keeping scenario, the suggested method demonstrates superior performance, exhibiting stable lane-keeping maneuvers and steering angles, as evidenced by the stable steering angle changes.  
Figure 7: Scenario 1, comparison of trajectories between TD3 + CER agent and TD3 agent.
## 5 Application
### Calculations  
Next, the relationships are analyzed, i.e., the effectiveness and ineffectiveness of each cybersecurity method against every cybercrime. We carry out the following mathematics. We present the following two IVCIFSs \(\hat{\text{E}}\) and \(\hat{\text{F}}\) representing the set of securities and the set of sources of threats, respectively. \[\hat{\text{E}}=\left\{ \begin{array}{l}\left(DD,[0.550,0.600]e^{i16[0.500,0.75 0]\pi},[0.300,0.400]e^{i16[0.125,0.313]\pi}\right),\\ \left(PP,[0.475,0.525]e^{i16[0.375,0.438]\pi},[0.250,0.375]e^{i16[0.125,0.250] \pi}\right),\\ \left(DCT,[0.375,0.525]e^{i16[0.438,0.563]\pi},[0.200,0.350]e^{i16[0.188,0.250] \pi}\right),\\ \left(ITSC,[0.400,0.500]e^{i16[0.313,0.438]\pi}[\frac{0.250,0.350]e^{i16[0.250,0.313]\pi}}{\left(SIEM,[0.300,0.375]e^{i16[0.563,0.613]\pi},[0.275,0.325]e^{ i16[0.188,0.313]\pi}\right)\end{array} \right)\end{array}\right.\]  
\[\hat{\text{F}}=\left\{ \begin{array}{l}\left(MD,[0.275,0.375]e^{i16[0.375,0. 500]\pi},[0.350,400]e^{i16[0.250,0.313]\pi}\right),\\ \left(USB,[0.375,0.450]e^{i16[0.250,0.375]\pi},[0.275,0.325]e^{i16[0.125,0.250] \pi}\right),\\ \left(RA,[0.350,0.400]e^{i16[0.375,0.438]\pi},[0.325,0.350]e^{i16[0.188,0.313] \pi}\right),\\ \left(WiFi,[0.450,0.550]e^{i16[0.313,0.500]\pi},[0.325,0.400]e^{i16[0.188,0.250] \pi}\right),\\ \left(HMI,[0.300,0.375]e^{i16[0.438,0.563]\pi},[0.200,0.300]e^{i16[0.063,0.188 ]\pi}\right),\\ \left(IC,[0.475,0.575]e^{i16[0.500,0.613]\pi},[0.250,0.350]e^{i16[0.125,0.313] \pi}\right),\\ \left(OC,[0.500,0.550]e^{i16[0.375,0.500]\pi},[0.225,0.300]e^{i16[0.063,0.250] \pi}\right),\\ \left(CN,[0.200,0.475]e^{i16[0.313,0.613]\pi},[0.250,0.375]e^{i16[0.125,0.188 ]\pi}\right)\end{array} \right)\] \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Source** & **Abbreviation** & **Membership** & **Non-Membership** \\ \hline Mobile Devices & \(MD\) & \([0.275,0.375]e^{i16[0.375,0.500]\pi}\) & \([0.350,0.400]e^{i16[0.250,0.313]\pi}\) \\ USB Ports & \(USB\) & \([0.375,0.450]e^{i16[0.250,0.375]\pi}\) & \([0.275,0.325]e^{i16[0.125,0.250]\pi}\) \\ Remote Access & \(RA\) & \([0.350,0.400]e^{i16[0.375,0.438]\pi}\) & \([0.325,0.350]e^{i16[0.188,0.313]\pi}\) \\ Wi-Fi & \(WiFi\) & \([0.450,0.550]e^{i16[0.313,0.500]\pi}\) & \([0.325,0.400]e^{i16[0.188,0.250]\pi}\) \\ HMI Interface & \(HMI\) & \([0.300,0.375]e^{i16[0.438,0.563]\pi}\) & \([0.200,0.300]e^{i16[0.063,0.250]\pi}\) \\ Internet Connections & \(IC\) & \([0.475,0.575]e^{i16[0.500,0.613]\pi}\) & \([0.250,0.350]e^{i16[0.125,0.313]\pi}\) \\ Outside Contractors & \(OC\) & \([0.500,0.550]e^{i16[0.3075,0.500]\pi}\) & \([0.225,0.300]e^{i16[0.063,0.250]\pi}\) \\ Corporate Networks & \(CN\) & \([0.200,0.475]e^{i16[0.313,0.613]\pi}\) & \([0.250,0.375]e^{i16[0.125,0.188]\pi}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Details of code penetration sources. To find out the efficacy of certain security measures against particular sources of code penetration, we use the Cartesian product. Thus, finding the Cartesian product between the IVCIFSs \(\hat{\mathbb{E}}\) and \(\hat{\mathbb{F}}\)  
\[\hat{\mathbb{R}}=\hat{\mathbb{E}}\times\hat{\mathbb{F}}\] \[\left[
# Spoofing Detection Using GNSS/INS/Odometer Coupling for Vehicular Navigation
## 4 Data Collection Scenarios  
Actual GPS and IMU data was collected using a vehicle in urban and suburban areas of Calgary. The experimental setup used is shown in Figure 3. Data was collected using tactical and MEMS grade IMUs whose specifications are given in Table 1. A navigation grade GNSS antenna was mounted on the vehicle roof and GNSS signals were passed to a two-way splitter. One branch was connected to a SPAN/LCI GNSS/INS system (NovAtel, Calgary, Alberta, AB, Canada) using the NovAtel's Inertial Explorer(tm) software in dual-frequency RTK mode with forward and backward smoothing to provide a sub-metre reference trajectory. The other branch was connected to a u-blox (EVK-6) receiver to provide GPS measurements. The IMUs used consist of 3-axis accelerometers and rate gyroscopes orthogonally mounted to each other.  
The \(z\) gyroscope was aligned with the vertical axis of the vehicle body frame and used for computing the azimuth angle. The \(x\) and \(y\) accelerometers were aligned with the forward and transversal axes of the vehicle body frame and used to calculate pitch and roll. The inertial sensors measurements are in the body frame. A rotation matrix from the body frame to the local level frame was estimated at the beginning. An on-board diagnostics (OBD) device was connected to the car OBD port and used to collect vehicle speed via a LG G3 cell phone. A cell phone application named Torque was used to collect vehicle speed. During the data collection process, the cell phone's various sensor outputs including GNSS navigation solutions and raw IMU values were also logged. The cell phone containing the MEMS IMU was installed on the back seat below the tactical IMU which was mounted on the vehicle roof.
## 6 Results
### Workload
The node give up the transmission of a packet if it senses the channel busy more than the maximum allowed. The explanations given above are valid for all the tested solutions. The cause of the different performance has to be found in the packet processing and memory allocation of each one. Regarding HTTP/UDP, its light and fast processing allows to this solution to achieve the best performance. The server is able to process fast the received requests and therefore augments the server rate. The HTTP/UDP processing also helps improving the client performance. The packets stay for less time in the buffer and, therefore, a new generated packet has more possibility to find space respect to other solutions. This is also the cause for the less pronounced drop that HTTP/UDP has respect to the CoAP based solutions. In Figure 9 one may observe that when the request rate become higher than 80, the success rate of TinyCoAP and HTTP/UDP undergo a pronounced drop. In fact, starting form this point the latency of a request/response transaction is comparable to the time taken by the client to generate and send a request. The probability of having a collision between a request and a response is therefore high. Starting from 150 requests per second, the drop is less sharp. The nodes are subjected to a workload close to its limits. The client rate can be augmented and the success rate will not lower as much as one may expect. As we explained above, these client rates saturate quickly the buffer. The sending process of many requests fails and therefore they never leave the node. The number of requests that are sent correctly became stable. As a consequence the server rate shows small variations. CoapBlip has the worst performance. The inefficient use of the RAM memory and the complex packet processing are the main limitation of its server rate. Moreover, the higher latency experienced by CoapBlip implies lower the point at which this become comparable to the client rate. As a consequence, packet collisions affect the server rate earlier than in the other solutions.
# Request Expectation Index Based Cache Replacement Algorithm for Streaming Content Delivery over ICN
## 4 Simulations and Results  
We implement the RXI cache replacement algorithm in the ndnSIM [42] simulator. We compare the proposed scheme with other cache replacement algorithms: LRU, LFU and TLP-TTH. To explicitly address the performance of the RXI algorithm, we adopt the default cache decision policy named Leave Copy Everywhere (LCE) to work cooperatively with the cache replacement algorithm.  
The simulation is implemented by using two topologies: a 5-level complete binary tree topology, as shown in Figure 5a, and a hybrid topology consisting of a core network and access networks, as shown in Figure 5b. The China Education and Research Network (CERNET) [43], which is the first nationwide education and research computer network in China, is selected as the core network, and we use a 3-level complete binary tree for the access networks.  
According to the Cisco Virtual Networking Index (CVNI) project [1], the video streaming delivery has become a major consumer of network traffic and is representative of streaming transmission. We therefore customize the chunk-by-chunk video streaming request model for our simulations in  
Figure 5: Simulation topologies. (**a**) hierarchical topology; (**b**) hybrid topology.  
both scenarios. Four metrics are adopted to evaluate the performance of the replacement policies in each scenario:  
1. _Average cache hit ratio_ is used as the primary performance metric to measure the cache utilization efficiency. In our simulation, the cache hit ratio is calculated individually by each router and represents the number of cache hits divided by the number of requests received by each router.
2. _Average hop count_ indicates the average transmission round-trip distance (the sum of the hops of one pair of request and data packets transferred) for each video chunk delivery.
3. _Network traffic_ represents the sum of the data forwarded by every ICN router during the entire simulation.
4. _Total retrieval time_ denotes the sum of the retrieval time between a request being sent and the data being received by the users.
# Optical Code Construction of 2D Spectral/Spatial BIBD Codes for SAC-OCDMA Systems
## 2 2D-BIBD Code for SAC-OCDMA Systems
### Construction of BIBD Code Families
#### 2.1.2 Proposed 2D Spectral/Spatial BIBD Code
When C2 is represented by \((2,\,5,\,6)\),
* the generated code for the second user becomes \((3,\,6,\,7)\). * Resulting 2D-BIBD codes are designed by using \(X_{h},Y_{g}\) and Equation (6) (line 25-line 26).
## 6 Threats to Validity  
As with any survey-based research, this study encounters validity challenges that warrant careful consideration. First, while we meticulously sourced studies from prominent databases (Google Scholar, IEEE, ACM, Springer, ScienceDirect, arXiv) and employed forward/backward snowballing to ensure broad coverage, the rapid evolution of NASGAN research poses risks of omitting cutting-edge works published after our search cutoff (early 2024) or in non-English venues. This limitation could skew trends or overlook novel methodologies, such as transformer-based architectures or energy-efficient NAS strategies. To mitigate this, we prioritized arXiv preprints and iterative snowballing, though findings remain reflective of literature up to the search period.  
Second, the systematic categorization of approaches inherently involves subjectivity. Classifying methods (e.g., distinguishing evolutionary algorithms from reinforcement learning) or search spaces (e.g., cell-based vs. chain-structured) relies on interpretative judgments, particularly when implementation details are ambiguously reported. Misclassification could distort comparative insights, such as overestimating search efficiency or misrepresenting architectural prevalence. To address this, we adhered to a predefined framework (Section 3.5) and cross-validated categorizations through dual-author reviews, resolving discrepancies via consensus.  
Third, our comparison framework emphasizes quantifiable criteria (e.g., GPU days, search space size) but excludes practical factors like code availability or hardware dependencies. This narrow focus may undervalue deployment feasibility; for instance, methods requiring specialized TPU clusters might achieve superior metrics but remain inaccessible to most researchers. We explicitly acknowledged this limitation in Sections 4.3.1 and 5.2, advocating for transparency in future work.  
Fourth, heavy reliance on CIFAR-10 and STL-10 for benchmarking introduces dataset bias. These datasets lack the complexity and diversity of real-world applications (e.g., medical imaging or high-resolution video generation), limiting the generalizability of conclusions. For example, architectures optimized for low-resolution images may underperform in domain-specific tasks. We highlighted this gap in Section 5.1 and urged evaluations on broader datasets like CelebA and LSUN.  
Finally, dependence on IS and FID as primary metrics risks \"metric hacking\", as these scores may not align with human perception or detect subtle mode collapse. This could lead to inflated claims of superiority for architectures tailored to optimize IS/FID rather than practical performance. To mitigate this, we emphasized integrating supplementary metrics (e.g., precision/recall, human evaluation) in Section 5.2.  
By explicitly addressing these threats--spanning literature coverage, categorization subjectivity, framework scope, dataset bias, and metric reliability--we aim to clarify the study's limitations while reinforcing the validity of its contributions. Future work should prioritize reproducibility, broader benchmarks, and holistic evaluation frameworks.
# A Review on the Recovery and Separation of Gallium and Indium from Waste
## 3 Methods of Recovering Gallium and Indium from Secondary Sources
### Ionic Liquid Use
These ILs were then used to selectively extract Ga(III) and In(III) from a solution with an initial pH ranging from 0.1 to 2.0. The highest separation coefficient between In(III) and Ga(III) was 87, and it was achieved by using ALi-PC with an initial pH of 1.0. Stripping of the loaded ALi-PC with hydrochloric and sulfuric acid selectively removed In(III) over Ga(III). However, only half of the Ga(III) loaded in ALi-PC was scrubbed by using a pure In(III) solution. Batch simulation experiments for three counter-current extraction stages showed that extracting In(III) using ALi-PC made it possible to completely separate both metal ions, with Ga(III) being left in the raffinate [95].
## 4 Results and Discussion
### Characterisation of Membranes
#### 4.1.2 Hydrophilicity of Surfaces  
Self-cleaning surfaces are either superhydrophobic or superhydrophilic, such as _Nelumbo nucifera_ (lotus leaves) and _Ruellia devosiana_. In water purification, hydrophilic surfaces are preferred, which can be achieved by changing both the surface roughness and the surface free energy [33; 34]. In order to evaluate the hydrophilisation effect of the three modification methods, the water contact angle of the membrane surfaces was measured after 5 s (Table 1).  
As shown in Table 1, the polydopamine coating slightly increases the contact angle of the membranes, which is similar to what was shown in the work of [PERSON] [34]. The reason for this is probably related to a composite solid-liquid-air interface, formed between the water droplet and the polyamide surface. When the nanoscale narrow valleys of the polyamide layer make contact with water droplets, water may not penetrate into the surface cavities, which results in the formation of air pockets and leads to a composite interface [35]. As discussed by [PERSON] and [PERSON] [36], the contact angle increases for a hydrophilic surface with an increase of the fractional flat geometrical area of the liquid-air interface under the droplet. Furthermore, the nanoscale asperities can pin water droplets and thus prevent water from filling the valleys between the asperities [35]. Due to these reasons, the contact angle of the polydopamine-modified membrane is larger than the one of the unmodified membrane.  
When the polydopamine layers further combine with TiO\({}_{2}\) nanoparticles, the surface free energy is greatly increased. This high surface free energy results in a significant reduction of the contact angle. For the self-assembly method, the TiO\({}_{2}\) clusters show the same behaviour as aforementioned.
## 3 Results and Discussion
### Analysis of CIM's Potential for the Calculation of the Indicators
#### 3.2.20 The Indicator for Effluent Management  
This indicator promotes the recharge of underground reserves, reducing the risk of flooding and the load on public drainage and effluent treatment systems. It also promotes the adequate dimensioning of domestic wastewater treatment systems, responding to the demands generated by the project. This is addressed by SBToolPT Urban, SNTool, BREEAM C, and LEED ND, and addressed by Levels, ISO 37120, and SDGs, making it a prioritized environmental concern. According to the SBToolPT Urban Guide, there are two parameters to calculate this indicator: the Percentage of Infiltration Areas and the Effluent and Soil Permeability Index. The first parameter is obtained from the ratio of Permeable Areas and the Total Intervention Area. It can be calculated through the CIM model, by determining the permeable areas, and then applying it in a Dynamo routine. The second parameter is determined by a verification checklist. For this stage, although the criteria can be added to the CIM model, the list is based on two plans, including Effluent Management Plan, and Water Management Plan, which cannot be assessed through the model. Thus, this parameter is considered \"NO\" regarding its applicability to CIM modeling.