esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Optimization Methods and Results
### Hardware System Optimization  
#### 3.2.1 Memory Access Optimization Method 1: Continuous Read Mode  
The CONV operation's output is written to DDR in burst mode, so the write addresses are continuous, as Figure 9a shows. However, the input sequence of the ARMP module is different from the output sequence of the CONV module. The read addresses are random, which is shown in Figure 9b. Figure 9c provides the principle of a usual solution. The read address of 29 bits is divided  
Figure 8: CNN network model design and parameter reduction. (**a**) The effects of the number of CONV layers on CNN model. (**b**) The effects of the ratio of output channels and input channels. (**c**) The effects of the number of sample points of input tensor. (**d**) The CNN model which has relative high accuracy, less required buffer size, and a few parameters. (**e**) The trend curves of loss and accuracy during training. (**f**) Visualization of FC1’s output tensor.  
into two parts: the part of bit 2 to bit 0 is the data mask to select the valid word in the 256 bits which are read back from DDR, and the other part of bit 28 to bit 3 is the aligned address which MIG operates. The timing diagram of the solution is shown in Figure 9d. However, the time to read back from DDR is far longer than the necessary time to consume, which leads to low data throughput and poor efficiency.  
To advance memory access speed, we design an optimized memory access solution, which is shown in Figure 9e. We employ a FIFO to buffer the data mask to nonstop transfer the aligned address to MIG and achieve a continuous read operation. The data which is read back is deposited in a D Flip-Flop (DFF) to keep the same time latency with signal data_valid. Then the valid word is chosen by the data mask and written into RDF. The timing diagram of the optimized solution is shown in Figure 9f. The optimized solution realizes continuous read and needs to wait MIG read back for only one time, saving plenty of time.  
Figure 9: Memory access module design and optimization. (**a**) Write CONV’s output to DDR. (**b**) Read CONV’s output from DDR. (**c**) Usual solution to discrete read address. (**d**) Timing diagram of usual solution to discrete read address. (**e**) Optimized solution to discrete read address. (**f**) Timing diagram of optimized solution to discrete read address.
# A Practical and Efficient Node Blind SignCryption Scheme for the IoT Device Network
## 4 Functionality Comparisons and Security Analysis
### Signer Fair Signature Operation  
Our proposed scheme can offer the signature of sensed data after the base station \(BS\) has received the encrypted sensed data from the user. In this time, \(BS\) only can apply the square root operation on these sensed data to generate the corresponding signature under these blind and encrypted data. In the online-signing operation, the IoT device can perform lightweight operations on the user's sensed data and obtain the signing result after the offline-signing phase performed by the signer \(BS\). From the two signing phases above, we know that the IoT device and the base station can present some random numbers in these phases to prevent the unfair situation that the signature generation is controlled by a certain party.
## 4 Results and Discussion
### Ethical and Environmental Considerations  
The rapid advancement and increasing computational demands of NAS methods applied to GANs highlight critical ethical and environmental concerns that must be thoroughly addressed. A major issue arises from the substantial computational resources required by NAS-GAN methods, leading to high energy consumption and significant carbon footprints. This environmental impact becomes particularly pronounced during extensive search processes, where numerous architecture evaluations are performed over prolonged periods. Such practices contribute to greenhouse gas emissions, exacerbating climate change concerns and contradicting sustainability objectives in AI research.  
To mitigate these environmental effects, there is a compelling need for developing and adopting energy-efficient NAS algorithms. Promising directions include incorporating techniques such as early stopping criteria, knowledge transfer across search spaces, and resource-aware optimization strategies. Additionally, research into NAS algorithms that leverage lower-energy hardware or renewable energy-powered infrastructure could considerably reduce ecological impacts.  
Beyond environmental sustainability, NAS-GAN methods raise ethical considerations tied to their deployment in sensitive or critical applications. Automated generation and deployment of GAN architectures, without rigorous oversight, risk amplifying biases or producing unintended discriminatory outcomes. For instance, GAN-generated data may inadvertently encode biases present in training datasets, reinforcing societal inequalities when used in domains like facial recognition, healthcare diagnostics, or autonomous decision-making systems.  
Ensuring fairness and transparency thus becomes paramount. Researchers and practitioners must commit to careful dataset curation, bias detection, and mitigation strategies in algorithmic design. Moreover, involving diverse stakeholder perspectives in developing standards and guidelines can help preempt ethical pitfalls and ensure responsible application of GANs.  
In summary, a conscientious approach to NAS-GAN development encompasses not only technical advancements but also deliberate ethical foresight and environmental responsibility. The community must strive towards standards that prioritize sustainability, fairness, and social accountability, ensuring that advancements in GAN architectures benefit society without exacerbating existing ethical or environmental issues.
# Robust Transceiver Design for IRS-Assisted Cascaded MIMO Communication Systems
### Contribution  
Motivated by the above, in this work, we consider a DF multi-hop system with multiple antennas at the source, relay and destination along with an IRS to provide additional links. Then we try to maximize the throughput rate for the worst-case RSI scenario. To the best of our knowledge, this is the first time that the throughput rate maximization against the worst-case RSI is evaluated for IRS-assisted DF full-duplex relay in MIMO systems. First, we simplify the problem by finding an analytical lower bound for the performance of the IRS. Then, the optimization of maximum achievable rate of the DF full-duplex relaying is cast as a non-convex optimization problem. Thereafter, we propose a low complexity method to find the solution using majorization theory. We propose an efficient algorithm to solve this problem in polynomial time. Finally, the transmit signal covariances at the source and the relay are designed efficiently to improve robustness against worst-case RSI channel in a given uncertainty bound. Notice that once the covariances are known, one can easily find the precoders using conventional methods such as singular-value decomposition (SVD), etc. To the best of our knowledge, this is the first work that uses the IRS for RSI cancellation in MIMO full-duplex DF relay systems.
# Computational Test for Conditional Independence
## 2 Computational Testing with Categorical Variables
### Choice of Classification Model and Performance Metric  
The computational method described above assumes that modeling the classification probabilities \(P(Y=k|X,Z)\) or \(P(X=j|Y,Z)\) allows us to extract all relevant information from the conditioned variables. Significantly, the method relies on the robustness of the classification model in generalizing from the training data to the testing data. Although an optimal classification model cannot be guaranteed, modern off-the-shelf tree-based ML algorithms such as XGBoost [[https://xgboost.readthedocs.io/en/stable/](https://xgboost.readthedocs.io/en/stable/)]([https://xgboost.readthedocs.io/en/stable/](https://xgboost.readthedocs.io/en/stable/)) (eXtreme Gradient Boosting) [19] have demonstrated remarkable performance in predicting class categories and robustness against overfitting training data. Other alternatives are random forest [20] or other gradient boosting frameworks such as LightGBM [21] or CatBoost [22].  
Since different metrics evaluate various aspects of a classification model's performance, choosing a performance metric carries implications in CCI testing. The metrics we use in this article are log loss and Kappa scores; these are well-known, continuous, and have somewhat different qualities. Kappa scores are better when comparing the same model across different datasets, especially when categories are unbalanced. Log loss offers greater precision than Kappa scores by accounting for the certainty of class predictions, which is relevant. We have yet to determine which metrics best suit the task; we will attempt to shed some light on this question in Section 3. Note that performance metrics are different from loss functions, which are necessarily differentiable. However, loss functions (such as log loss) can also be used as performance metrics.
# Drying Characteristics of Chicken Manure Under a Variable Temperature Process
## 3 Results and Discussion
### Drying Characteristics at Different Drying Temperatures
concluded that drying time was reduced and the energy consumption was decreased by increasing the temperature in the sludge drying process using hot air [20]. The difference between the method in this study and the traditional drying method was that the energy consumption in the first drying stage was calculated based only on the fan's energy consumption. Therefore, the conclusions described above were reached. As shown in Figure 5, the total nitrogen loss of chicken manure in different experimental groups showed no significant difference (\(p>0.05\)). The primary reasons for this phenomenon may include the following: Nitrogen primarily exists in the form of NH\({}_{4}\)-N and Org-N. Among these, NH\({}_{4}\)-N (including NH\({}_{3}\) and NH\({}_{4}\)\({}^{+}\)) mainly originates from the decomposition of uric acid and undigested proteins. In the early drying stage, the water content of chicken manure is relatively high, and a large amount of NH\({}_{4}\)-N is dissolved in the water [21; 22; 23]. As the moisture content decreases to approximately 45%, most NH\({}_{4}\)-N is released due to its volatile nature. At this point, the remaining nitrogen in chicken manure primarily exists as organic nitrogen. Despite the fact that an increase in the drying temperature can reduce the drying time, the degradation of uric acid and the generation of NH\({}_{4}\)-N remain very limited in the second drying stage [6]. Therefore, higher drying temperatures cannot effectively reduce nitrogen loss. As a result, the drying temperature in the second stage had no significant effect on nitrogen loss during the chicken manure drying process (\(p>0.05\)). The above results are consistent with those of [PERSON] [21]. As the temperature continued to increase, a higher drying temperature was able to rapidly reduce the water content in the laying-hen manure to lower than 30%, resulting in the quick decrease in the activities of the microorganism, thus protecting the laying-hen manure against further aerobic fermentation and leading to the fixation of more organic nitrogen in the laying-hen manure. Considering the effects of drying temperature in the second stage on both the drying rate and energy consumption, the optimal drying temperature for the second stage was determined to be 55 \({}^{\circ}\)C.  
Figure 4: Effect of drying temperature in the second stage on energy consumption of the variable temperature process. Notes: Different letters (a, b, c) are significant (\(p<0.05\)). The same letters are not significant.
## 3 Results and Discussion  
### Fresh Properties of Concrete  
The fresh properties of concrete were recorded for each batch. The tested fresh properties of the concrete included slump and unit weight, as shown in Figure 3. The slump value ranged from 16 to 21 cm. In the control group, the slump values of the C30 and C50 mixes were 20 and 21 cm, respectively. These results showed that the mixtures of the control group had good workability. In the experimental group, the slump value of the E30-S and E50-S mixes with steel fiber was 18 cm, and the slump value of the E50-P mix with polypropylene fiber was 20 cm, but the slump value of the E50-M mix with steel fiber and polypropylene fiber was only 16 cm because the amount of fiber used in the E50-M mix was higher, resulting in a stiffer mixture and leading to reduced workability. For low strength LWC, the unit weights of the C30 and E30-S mixes were 1875 and 1953 kg/m\({}^{3}\), respectively, which are not much different. For medium-strength LWC, the unit weight of the C50, E50-S, E50-P, and E50-M mixes with the addition of fiber ranged from 2039 to 2118 kg/m\({}^{3}\), which meets the limit of unit weight for structural LWC.
# A Review of Double-Walled and Triple-Walled Carbon Nanotube Synthesis and Applications
## 2 Synthesis and Characterization of DWNTs
### Brief Summary: Synthesis of DWNTs  
As discussed above, it seems that a recipe to controllably-produce DWNTs relative to their diameter range, metallicities, flavors and chiralities does not yet exist. Each approach discussed above leads to different outcomes, which present advantages and disadvantages. The arc-discharge method has the advantage of producing ultra-high quality DWNTs, but it has the disadvantages of producing many other forms of carbon materials, and the samples that are grown via this method are very hard to purify. The CCVD method can be applied using different approaches: the support catalyst method and the floating catalyst method. The support catalyst method has the advantage of selecting the DWNT diameter range, metallicity and number of walls by controlling the properties of the metal nanoparticles used as precursors. On the other hand, the floating catalyst method does not provide as good control over the diameter range and metallicity, but provides a better control over the number of walls of the carbon nanotube when sulfur is added to the growth reaction zone. Moreover, in this approach, the precursor is supplied continuously, which results in a continuous production of DWNTs. Both methods have disadvantages inherent to the chemical vapor deposition methods: any small change in the growth parameters will dramatically change the growth outcome, and the quality of the tubes is lower in comparison to the pea pod-derived method and the arc-discharge method. Finally, the pea pod-derived method produces high quality DWNTs and is a good candidate to producing chirality-specific DWNTs. However, the method produces DWNTs in a limited range of diameters with the outer tube diameter always larger than 1 nm, which is a necessary condition to encapsulate molecules that will later become the inner tube. The authors also recommend that the readers read reference [143], which is a very good complementary reference about many aspects of DWNTs' production and purification.
## 3 Results and Discussion
### Principal Component Analysis (PCA)  
The principal component analysis enabled the presentation of the relationships between the samples in one chart. The PCA was based on all research results. The chart below (Figure 8) shows the results of the PCA based on correlation for the two samples of model soups (rabbit cream soup and rabbit cream soup with beef balls) stored for 21 days in two variants of texture analysis (i.e., cold and hot). PC 1 (57.05%) and PC 2 (23.24%) explained 80.29% of the total variance.  
The firmness and consistency results formed separate clusters, indicating a positive correlation. Moreover, neither the firmness nor consistency were correlated with the type of sample or storage time. The loading signs indicate that the firmness and consistency were negatively correlated with the method of preparation (different test temperatures) and cohesiveness. There was a rather weak positive correlation between the viscosity index and the consistency values. The viscosity index was negatively correlated with the storage time. The values of the viscosity index tended to increase along with the soup storage period. On the other hand, the heating of the samples resulted in higher cohesiveness but a lower firmness and consistency.  
Figure 7: Changes in the modulus of elasticity \(G^{\prime}\) of the model systems during heating. Values expressed as means \(\pm\) SD (n = 6).  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Type of Sample** & **Temperature** & **1 Day** & **7 Day** & **14 Day** & **21 Day** \\ \hline \multirow{2}{*}{Soup with rabbit meat} & 20 \({}^{\circ}\)C & 37.63 \({}^{\mathrm{a}}\)\(\pm\) 1.61 & 33.24 \({}^{\mathrm{b}}\)\(\pm\) 1.42 & 29.11 \({}^{\mathrm{c}}\)\(\pm\) 1.13 & 25.38 \({}^{\mathrm{d}}\)\(\pm\) 1.12 \\  & 55 \({}^{\circ}\)C & 18.21 \({}^{\mathrm{f}}\)\(\pm\) 0.98 & 16.14 \({}^{\mathrm{g}}\)\(\pm\) 0.75 & 15.23 \({}^{\mathrm{g}}\)\(\pm\) 0.58 & 13.22 \({}^{\mathrm{h}}\)\(\pm\) 0.72 \\ \hline \multirow{2}{*}{Soup with rabbit meat and beef balls with carrots} & 20 \({}^{\circ}\)C & 29.03 \({}^{\mathrm{c}}\)\(\pm\) 1.11 & 27.47 \({}^{\mathrm{cd}}\)\(\pm\) 1.31 & 25.54 \({}^{\mathrm{d}}\)\(\pm\) 1.26 & 22.30 \({}^{\mathrm{e}}\)\(\pm\) 0.87 \\  & 55 \({}^{\circ}\)C & 16.21 \({}^{\mathrm{g}}\)\(\pm\) 0.84 & 15.75 \({}^{\mathrm{g}}\)\(\pm\) 0.67 & 13.02 \({}^{\mathrm{h}}\)\(\pm\) 0.70 & 11.69 \({}^{\mathrm{i}}\)\(\pm\) 0.54 \\ \hline \end{tabular} (a) Different superscript letters indicate a significant difference at \(p\) \(<\) 0.05 (mean \(\pm\) SD; n = 6).  
\end{table}
Table 3: The dynamic viscosity \(\eta\) (Pas) of the cream soups.
## 2 Materials and Methods
### The Experimental Facility
The experimental facility for test purposes has been specifically designed and built. The adsorption reactor has been integrated in a hydraulic plant piping (AISI tubes). The plant consists of:  
* Zeolite reactor with mechanical support,
* Heat pump chiller for cooling (condensation) source, with an electrical power of 10 kW,
* Electric heater (2 resistances for 10 kW total power) for heating (evaporation) purposes,
* 4 tanks with 300 L of capacity for thermal flow management,
* Vacuum dry-scroll pump,
* Metallic skid for whole system,
* Hydraulic pumps for HTF flows, flow meter with an accuracy equal to \(\pm\)5% and electrical valve for plant operation. In Figure 5, the experimental set-up is shown. Figure 4: Thermocouple for temperature data acquisition within the zeolite bed. Figure 5: Experimental set-up. The plant is able to test the desorption/adsorption phase in different conditions: the internal pressure during the condensation/evaporation process is imposed through a strict control of temperature and mass flow of HTF inside the coil tube on lateral plates or in a water pool. In order to start with the experimental phase, the whole reactor chamber is evacuated up to the target internal pressure through the vacuum pump and the internal pool is filled at the proper level. During the charge phase (desorption), the reactor is heated up with HTF at constant mass flow and inlet temperature (by automatic control of electric heater). After increasing due to initial desorption, the internal pressure is kept at a value as constant as possible acting on the condensation pool or plate through tuning the mass flow into the coil tubes. The thermal tanks (for reactor heating and vapour condensation) have been previously charged at the target temperature. All the parameters are registered for physical quantities' evaluation. After a storage time, the discharge phase is carried out: the reactor is cooled down with HTF at constant mass flow and inlet temperature (by automatic control of chiller),and the evaporation and the vapour adsorption take place with a constant internal pressure, in the same way as during the condensation process. The physical quantities to be evaluated are:  
* Discharged Power (W): \[DP=\frac{\int_{0}^{t_{ads}}\dot{m}_{HTF}\;c_{p}(T_{out,HTF}-T_{in,HTF})dt}{t_ {ads}}\] (1)
* Stored Energy (Wh): \[SE=\int_{0}^{t_{ads}}\dot{m}_{HTF}\;c_{p}(T_{out,HTF}-T_{in,HTF})dt\] (2) where \(t_{ads}\) is the time duration of the adsorption process, \(\dot{m}_{HTF}\) and \(c_{p}\) are the mass flow and the specific isobaric heat capacity of HTF in the finned tube respectively, and \(T_{out,HTF}\) and \(T_{in,HTF}\) are the HTF temperatures at the reactor outlet and inlet section, respectively.
###### Abstract  
A high-throughput layered decoder for quasi-cyclic (QC) low-density parity-check (LDPC) codes is required for communication systems. The preferred way to improve the throughput is to insert pipeline stages and increase the operating frequency, which suffers from pipeline conflicts at the same time. A priority-based layered schedule is proposed to keep the updates of log-likelihood ratios (LLRs) as frequent as possible when pipeline conflicts happen. To reduce pipeline conflicts, we also propose double update queues for layered decoders. The proposed double update queues improve the percentage of updated LLRs per iteration. Benefiting from these, the performance loss of the proposed decoder for the fifth generation (SG) new radio (NR) is reduced from 0.6 dB to 0.2 dB using the same quantization compared with the state-of-the-art work. As a result, the throughput of the proposed decoder improved up to 2.85 times when the signal-to-noise ratio (SNR) was equal to 5.9 dB.  
double update queues; high throughput; pipeline conflicts; QC-LDPC; priority-based +
Footnote †: journal:  
**Citation:** [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] High Throughput Priority-Based Layered QC-LDPC Decoder with Double Update Queues for Mitigating Pipeline Conflicts.  
_Sensors_**2022**, 22, 3508. [[https://doi.org/10.3390/s2093508](https://doi.org/10.3390/s2093508)]([https://doi.org/10.3390/s2093508](https://doi.org/10.3390/s2093508))
# Analytical Modeling for Underground Risk Assessment in Smart Cities
## 6 Implementation of Approximation Schemes for Underground Risk Index
### Hierarchical Fuzzy Inference System Based Approximation  
We implemented the two selected models as shown in Figures 6 and 7 in MATLAB for evaluating results of hierarchical FIS. For all fuzzy inference sub-systems, we used the Mamdani-type fuzzy inference system which follows a four-step process to compute the crisp output from given inputs. A very useful tutorial on the internal working of Mamdani-type fuzzy inference system can be found in [34]. Table 3 presents the settings used in each step of Mamdani-type fuzzy inference system.  
Range of risk index values of selected risk contributing factors is \([0,10]\) which is covered by five overlapping triangular membership functions with details given in Table 4. The same scheme is also used for output, i.e., final risk index.  
Figure 8: Risk mapping of three sample cases using hybrid approach.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**FIS Component** & **Membership Function** & **Fuzzification** & **Implication** & **Aggregation** & **Defuzzification** \\ \hline Method used & Triangular & Min for AND Max for OR & Min & Max & Centroid \\ \hline \hline \end{tabular}
\end{table}
Table 3: FIS Components Configuration.  
Every model is separately evaluated twice: (a) average based scheme for rule definition; and (b) maximum based scheme for rule definition. Illustration of both schemes is given in Table 2 for three sample rules. After fuzzification process, antecedent of each rule is computed by selecting minimum (or maximum) value if the inputs of the rule are combined via AND (or OR) operation. Minimum operation is used in implication step to compute consequent of the rule from given antecedent values. In the third step, aggregation is performed using maximum operation to combine resultant consequent of all rules. Finally, centroid method is applied to compute crisp output in the last defuzzification step.