esa-sceva/satcom-chunk-collection huggingface dataset:
# Towards a Scalable Software Defined Network-on-Chip for Next Generation Cloud
## 4 No Codaptability
### Communication Protocol  
The single-flit packet structure does not permit encoding in the packet header the information for issuing control signals to configure the network and read per link counter statistics. Thus, such information must be carried within the packet payload.  
We adopted a communication protocol to avoid consuming bits by reserving part of the payload for control information. Here, the control packets are signalled by sending an initial _starting_ flit. Since a payload with an all bits set (i.e., the payload is 0 xFFFFFFFF) is rare, we use such value to enable the transmission of a subsequent set of control flits. Furthermore, a simple combinatorial logic can be used to detect such packets. Conversely, when such data flit must be transmitted to the destination, a pair of flits with the 0 xFFFFFFFF payload is transferred. Preliminary simulations confirmed to us that the penalty is negligible, especially when real application traffic is taken into account. To deal with such protocol, the routing logic is augmented with simple FSMs that can process configuration commands or performance counter readings. Table 1 shows the flit sequences (packets) exchanged between the source and destination nodes. All of the sequences comprise the starting flit followed by the flit specifying the command. We used \"don't care\" notation (X) to indicate content that depends on the selected destination target. This means that a group of bits in the command flit can serve as a mask to select the router or one of the RSs as the final target. The protocol ensures that configuration flits can be correctly delivered without sacrificing bandwidth for the application traffic. For instance,  
Figure 7: Router micro-architectural extension for supporting dynamic reconfiguration (**left**). An example of the reconfiguration flow is also depicted (**right**).  
the internal router port mapping requires sending four consecutive flits, while the configuration of VT-LUTs requires up to 88 flits each (here, we also included the starting and command flits).
## 3 Results and Discussion
### Composition of Fatty Acids in Raw and lyophilized Meat By-Products
**} \\ \hline C18.2n6c & 19.38 \(\pm\) 0.05 & 19.35 \(\pm\) 0.08 & 12.27 \(\pm\) 0.09 & 13.08 \(\pm\) 0.02 & 14.05 \(\pm\) 0.08 & 14.09 \(\pm\) 0.04 & 10.81 \(\pm\) 0.02 & 10.88 \(\pm\) 0.03 & * \\ C18.3n6c & 0.73 \(\pm\) 0.03 & 0.75 \(\pm\) 0.06 & 0.55 \(\pm\) 0.06 & 0.66 \(\pm\) 0.03 & 0.36 \(\pm\) 0.01 & 0.38 \(\pm\) 0.01 & 2.07 \(\pm\) 0.02 & 2.09 \(\pm\) 0.01 & * \\ C18.3n3 & 0.24 \(\pm\) 0.02 & 0.32 \(\pm\) 0.03 & 0.21 \(\pm\) 0.05 & 0.16 \(\pm\) 0.05 & 0.06 \(\pm\) 0.01 & 0.06 \(\pm\) 0.01 & 0.11 \(\pm\) 0.01 & * \\ C20.3n3 & 1.30 \(\pm\) 0.03 & 1.10 \(\pm\) 0.06 & 0.76 \(\pm\) 0.05 & 0.78 \(\pm\) 0.05 & 0.22 \(\pm\) 0.03 & 0.13 \(\pm\) 0.01 & 0.67 \(\pm\) 0.03 & 0.70 \(\pm\) 0.05 & * \\ C22.4n6 & 0.85 \(\pm\) 0.03 & 0.78 \(\pm\) 0.06 & 0.29 \(\pm\) 0.03 & 0.22 \(\pm\) 0.01 & 0.24 \(\pm\) 0.01 & 0.34 \(\pm\) 0.03 & 1.84 \(\pm\) 0.03 & 1.86 \(\pm\) 0.05 & * \\ C20.5n3 & 1.14 \(\pm\) 0.06 & 1.18 \(\pm\) 0.02 & 0.40 \(\pm\) 0.02 & 0.42 \(\pm\) 0.05 & 0.61 \(\pm\) 0.01 & 0.76 \(\pm\) 0.03 & 0.11 \(\pm\) 0.03 & 0.12 \(\pm\) 0.01 & * \\ C22.6n3 & 0.52 \(\pm\) 0.03 & 0.64 \(\pm\) 0.01 & 0.21 \(\pm\) 0.01 & 0.22 \(\pm\) 0.03 & 0.13 \(\pm\) 0.03 & 0.12 \(\pm\) 0.01 & 0.21 \(\pm\) 0.03 & 0.25 \(\pm\) 0.03 & * \\ Omega 6 & 20.97 \(\pm\) 0.5 & 2.12 \(\pm\) 0.16 & 1.36 \(\pm\) 0.23 & 1.39 \(\pm\) 0.21 & 1.466 \(\pm\) 0.21 & 1.56 \(\pm\) 0.12 & 1.473 \(\pm\) 0.12 & 14.83 \(\pm\) 0.13 & * \\ Omega 3 & 3.22 \(\pm\) 0.05 & 4.42 \(\pm\) 0.08 & 1.37 \(\pm\) 0.03 & 1.06 \(\pm\) 0.02 & 0.92 \(\pm\) 0.02 & 1.07 \(\pm\) 0.02 & 1.10 \(\pm\) 0.02 & 1.19 \(\pm\) 0.06 & * \\ Omega 6 & Omega 3 & 6.50 & 5.03 & 9.94 & 1313 & 1.591 & 14.58 & 13.99 & 12.46 & \\ Total & 24.19 & 25.43 & 14.97 & 15.02 & 15.58 & 16.67 & 15.83 & 16.02 & \\  & & & & & & & & & \\  & & & & & & & & & &In order to achieve economic viability and sustainability in the future, it is important to maximize the consumption of animal by-products. According to [PERSON] et al. [42], the utilization of by-products could reduce costs and help to deal with climate change. The content of omega FA in meat by-products has biological, biochemical, pathological, and pharmacological effects [43]. Meanwhile, omega 6 FA can be harmful if consumed in large amounts. An imbalance between these FAs can lead to inflammatory processes in the body and increase the risk for chronic diseases [44; 45]. [PERSON] et al. [46] showed in their research that an imbalance of omega fatty acids is associated with depression. According to [PERSON] [47; 48] the ratio of omega 6 to omega 3 changed from the recommended 1:1 or 1:2 for modern humans to 1:20, due to the Western diet. In our research, the omega 6 and 3 FA ratio was similar to the recommendation: in sheep by-products, particularly liver, after lyophilization the ratio was 1:2.65.
# Microbes as Medicines: Harnessing the Power of Bacteria in Advancing Cancer Treatment
## 2 Mechanism of Tumor Suppression
### Genetically Engineered Bacteria for Anti-Cancer Therapy
#### 2.2.3 Bacterial Vectors for Anti-Cancer Cargo  
Bacteria serve as vehicles for a wide array of therapeutic payload in the form of DNA, RNA, or protein to be delivered to the tumor cells. The intracellular bacteria are engineered to mediate a plasmid-based gene, carrying gene expression cassettes that regulate the expression of therapeutic proteins inside the tumor [47; 48]. Induction of apoptosis in tumor cells using ligands such as TNF-\(\alpha\), Fas ligand (FasL), TNF-related apoptosis inducing ligands (TRAIL) is an interesting therapeutic approach; however, toxicity and short circulation half-life after systemic administration limits this approach. This limitation can be addressed by using engineered bacterial strains that can help home apoptosis inducing ligands effectively into tumor cells [48]. Various studies have confirmed the anti-cancer potential of engineered bacteria expressing death inducers [10; 59; 60]. _S. typhimurium_ was successfully engineered to secrete murine FasL, a pro-apoptotic cytokine, and demonstrated reduced tumor growth in murine breast carcinoma and CT-26 colon carcinoma cells [61]. In another study, _S. typhimurium_ was designed to secrete TRAIL which induced tumor growth suppression in mice bearing melanoma tumor [10]. In both the cases, tumor growth was suppressed by induction of caspase-3 mediated apoptosis. Table 2 summarizes the applications of genetically engineered bacteria as a vector for anti-cancer treatment.
## 2 Background and Literature Review
### SDN Model for NDN
#### 2.2.1 A. Infrastructure Layer  
The infrastructure layer contains a number of network components that may exchange information and connect with one another, such as switches and routers. These devices carry out two tasks: they gather network status data, temporarily store it, and relay it to the controller. In addition, the second step entails processing the packet in accordance with the precise rules that the controller applies to the packets [20].
## 2 Mechanical Analysis Model for TSWs
### Determining Coefficients for Deflection Expressions with Additional Terms
\begin{array}{l}w|_{z=0,h}=0,\left.\frac{\alpha_{\text{m}}}{\alpha_{\text{ z}}}\right|_{z=0,h}=0\\ w|_{y=0,L}=0,\left.\frac{\alpha_{\text{m}}}{\alpha_{\text{y}}}\right|_{y=0,L}= 0\\ \end{array}  \tag{14}\]  
Consider the left edge of the rectangular thin plate. The boundary's condition on this edge is both fixed and supported, resulting in an angular displacement of zero at (\(y=0\)). This implies that the partial derivative of deflection \(w\) with respect to \(y\) must also be zero at this boundary:  
\[\left.\frac{\partial\omega}{\partial y}\right|_{y=0}=\sum\limits_{m=1}^{\infty }\sum\limits_{n=1}^{\infty}\frac{m\pi}{L}b_{mn}\sin\frac{n\pi z}{h}+\sum \limits_{n=1}^{\infty}\left[\frac{L}{6D}(2A_{n}+B_{n})\right]\sin\frac{n\pi z} {h}+\sum\limits_{m=1}^{\infty}\frac{m\pi}{DL}F_{m}(z) \tag{15}\]  
By expanding \(z\), \(z^{2}\), and \(z^{3}\) into a sinusoidal series, Formula (15) can be simplified to:\[\frac{\partial\omega}{\partial y}\bigg{|}_{y=0}=\sum_{n=1}^{\infty}\left[\sum_{m=1}^ {\infty}\frac{m\pi}{L}b_{mm}+\frac{L}{6D}(2A_{n}+B_{n})+\frac{2h^{2}}{D\pi^{3}n^ {3}}\sum_{m=1}^{\infty}\left(C_{m}+\left(-1\right)^{n+1}D_{m}\right)\frac{m\pi }{L}\right]\sin\frac{n\pi y}{h} \tag{16}\]  
The left edge of the rectangular thin plate (\(y=0\)) is a fixed and supported edge with zero angular displacement. By setting Formula (16) equal to zero, the following equation is obtained:  
\[\frac{\pi}{a}\sum_{m=1}^{\infty}mb_{mm}+\frac{L}{6D}(2A_{n}+B_{n})+\frac{2h^{2 }}{D\pi^{2}n^{3}L}\sum_{m=1}^{\infty}m\left(C_{m}+\left(-1\right)^{n+1}D_{m} \right)=0 \tag{17}\]  
Similarly, the equations for the remaining three edges concerning the coefficients \(B_{n}\), \(C_{m}\), and \(D_{m}\), to be determined, respectively.
## 2 Materials and Methods
### Signal Processing  
Data were acquired from the 10 muscles using wireless sensors (Trigno, Delsys, Natick, MA, USA). In all sessions involving EMG, the raw signals were collected at a sampling rate of 2 kHz. The EMG system used in this study has a default bandwidth of 20-450 Hz, which adheres to conventional EMG preprocessing. After the rectification process of the collected EMG signals, we applied a Butterworth fourth-order low-pass filter with a cutoff frequency of 5 Hz as the smoothing process in order to analyze the envelope of muscle activation. Additionally, the sensors measured the foot acceleration changes in the sagittal plane because they had a built-in accelerometer. The sensors were attached to the instep and heel of both shoes (Figure 1). The acceleration signals were collected at a sampling rate of 148.148 Hz and smoothed using a Butterworth fourth-order low-pass filter with a cutoff frequency of 10 Hz. The toe-off timing was extracted from the change in acceleration at the instep, and the heel strike timing was extracted from the change in acceleration at the heel. The gait cycles were divided based on these points. Each session in which the signal was measured was a session in which the GF was changed, to consider the participant's adaptation; therefore, the EMG data of the last 100 gait cycles from each session were extracted.
## 4 Numerical Simulations
### Simulation I: CIR Estimation in Single-Carrier System
Figure 4 shows the variation in NMSD with a different number of measurements, which further highlights the robust performance of the proposed method in impulsive noise environments. Table 1 summarizes the average number of iterations to reach stop criteria, runtime for single estimation, and converged NMSD values. While the proposed non-monotone strategy requires longer computation due to the extra proximal step, it excels in convergence speed and estimation accuracy in both AWGN and impulsive noise environments compared to the mADMM algorithm. Figure 5 displays the NMSD across various SNR levels. In AWGN environments, with precise channel sparsity information, the OMP algorithm obtains the lowest NMSD at high SNR levels (\(\geq\)10 dB), while other algorithms perform similarly. However, in impulsive noise environments, both the mADMM and nmADMM algorithms outperform other competing algorithms by over 15 dB across all SNR levels. Notably, the proposed nmADMM algorithm achieves lowest estimation error under impulsive noise conditions as well as in AWGN environments at low SNR levels (\(\leq\)10 dB). Figure 1: Estimated CIR in AWGN environment: (**a**) OMP, (**b**) FISTA, (**c**) ADMM, (**d**) IPM-PCG, (**e**) mADMM, and (**f**) nmADMM. Figure 3: Comparison of convergence trends of NMSD across iterations: (**a**) AWGN environment and (**b**) impulsive noise environment. Figure 2: Estimated CIR in impulsive noise environment: (**a**) OMP, (**b**) FISTA, (**c**) ADMM, (**d**) IPM-PCG, (**e**) mADMM, (**f**) nmADMM.
## 4 Proposed BiGR Approach
### The BiGR Layers Architecture Analysis  
The layer stack architecture analysis of our proposed BiGR approach is visualized in Figure 8. The layer stack architecture contains the details about each active layer with the input and output shapes. The first layer of the architecture is the input embedding layer. The input layer has the (None, 3) input and output shape with the float data type. The input layer is then passed to the next embedding layer present in the stack. The embedding layer has the input shape of (None, 3) and an output shape of (None, 3, 64). Then, a bidirectional layer of the Gated recurrent unit layer received the output shape of (None, 3, 64) from the previous layer as input and has an output shape of (None, 3, 1024). The next layer in the architecture stack is the Recurrent neural networks layer, which has an input shape of (None, 3, 1024) and an output shape of (None, 1024). Next, a family of dense layers is involved in architecture sequentially. The architecture's last layer is the model's output layer with an input shape of (None, 32) and an output shape of (None, 1). The novel proposed BiGR architecture is deeply analyzed to understand the model layer stack.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Sr no.** & **Model Layers** & **Unit** & **Activation** & **Output Shape** & **Parameters** \\ \hline
1 & The Feature Embedding layers. & 50,000 & N/A & (None, 3, 64) & 3,200,000 \\
2 & The Bidirectional-Gated recurrent unit layers. & 512 & N/A & (None, 3, 1024) & 1,775,616 \\
3 & The Recurrent neural networks layers. & 1026 & N/A & (None, 1024) & 2,104,324 \\
4 & The Dense layers. & 512 & RELU & (None, 512) & 525,824 \\
5 & The Dense layers. & 512 & RELU & (None, 512) & 262,656 \\
6 & The Dense layers. & 128 & RELU & (None, 128) & 65,664 \\
7 & The Dense layers. & 128 & RELU & (None, 128) & 16,512 \\
8 & The Dense layers. & 64 & RELU & (None, 64) & 8256 \\
9 & The Dense layers. & 64 & RELU & (None, 64) & 4160 \\
10 & The Dense layers. & 32 & RELU & (None, 32) & 2080 \\
11 & The Dense layers. & 32 & RELU & (None, 32) & 1056 \\
12 & The Dense layers. & 1 & SIGMOID & (None, 1) & 33 \\ \hline \hline \end{tabular}
\end{table}
Table 6: The Deep BiGR model hyperparameter configuration parameters.
## 4 Results
### Scalability of the Model  
To evaluate the scalability of our fine-tuned English-Twi model, we examine its performance across different sizes of training data.  
Figure 4 highlights the model's improvement as the training data size increases. With only 500 pairs, the model's performance is significantly lower, especially in SpBLEU and ROUGE-1. As the data size grows to 1000 and 2000 pairs, there is a noticeable improvement in translation quality, particularly in SpBLEU and ROUGE-1. Most errors were grammatical or involved rare vocabulary, indicating room for future work with a larger corpus. The whole dataset exhibits the biggest improvement, with SpBLEU rising to 71.30%, ROUGE-1 reaching 65.24%, and WER falling to 68.32%.
# Fast Frequency Acquisition and Phase Locking of Nonplanar Ring Oscillators
## 3 Principle of Frequency Acquisition
For a smaller division ratio, to get the same MA, a shorter frequency measuring period can be adopted, and a faster acquisition can be achieved. However, with the MCU used in this work, the MA is increased. At the initial frequency difference that equals MA, the phase locking time is longer under the function of the loop filter. If the phase locking time is longer than one frequency control cycle, the OPLL will become unstable. For example, if the division ratio is set at 100, a frequency measuring period can be set at 2.5 ms, and one frequency control cycle is 3.5 ms, with an MA of 80 kHz. At an initial frequency difference of 80 kHz, the phase locking time is close to 3.5 ms, and the loop operates at the critical region with poor stability. Moreover, for a higher division ratio, the MMF is decreased, which is also undesired. Thus, the optimum division ratio in this work is 200. If we could decrease the phase locking time, under the condition of a shorter loop delay and a wider loop bandwidth, a smaller division ratio  
Figure 3: Flow chart of frequency discrimination and control program. is allowed. The output voltage range of the D/A converter is \(\pm\)5 V. The converted signal has a total amplification of 54\(\times\) before sending to the PZT port of the local laser.
## 3 Results
### Hardness Distribution  
Hardness measurements were conducted on the AW joints, the PWCR joints, the PWSHT joints, and the PWCR-SHT joints, spanning from the FZ center to the BM, as shown in Figure 9. For the AW joints, both the FZ and the HAZ exhibit lower hardness values than the BM, with the most insufficient hardness recorded within the FZ, ranging from 61.7 HV to 65.3 HV. A significant increase in hardness occurs from the FZ to the HAZ, marked by an \"inflection point\" in the hardness distribution, signifying softening within the HAZ [24]. Subsequently, as the distance from the FZ center increases, hardness gradually approaches that of the BM, resulting in inconsistent mechanical properties across the FZ, the HAZ, and the BM regions. Comparatively, the PWCR joints exhibit a noteworthy enhancement in hardness, with a 42.67% increase in the FZ hardness attributed to the strain-hardening effect induced by the CR. As one moves away from the FZ center, hardness gradually decreases. The softened zone within the HAZ remains the lowest point in the overall joint hardness, followed by a gradual approach to the BM hardness, resulting in uneven mechanical properties. The PWSHT joints, on the other hand, experience a notable reduction in overall hardness. The HAZ exhibits a hardness range of 61.3 HV to 64.2 HV, while the BM demonstrates a range of 60.8 HV to 64.1 HV. Consequently, the mechanical properties of the HAZ and the BM tend to converge, but the FZ maintains the lowest hardness range, ranging from 56 HV to 61.5 HV. In contrast, the PWCR-SHT joints exhibit hardness levels ranging from 56.9 HV to 62.3 HV across the FZ, the HAZ, and the BM, showcasing a more consistent overall mechanical performance within the joints.
## 2 Methodology
### Orbital Configuration and Sensor Type  
In short, the orbital configuration mainly depends on satellite altitude and inclination angle. Orbital configuration and swath width determine the repeat cycle of the sensor viewing extent. To represent typical (i.e., polar-orbiting, sun-synchronous) configurations, six different sensors are selected here to represent a range of hypothetical instruments including PMW radiometers, SARs, and LiDARs.
## 3 Methodology
### Implementation Considerations  
Our experiments confirm LSTM's superior performance, though several practical considerations exist. The architecture requires approximately 15% more training time than GRU, and its larger parameter count necessitates more training data. The architecture also benefits from careful hyperparameter tuning. These trade-offs are justified by the significant improvements in prediction accuracy and the critical nature of precise sanitary facility planning, where even small improvements in accuracy can have substantial practical impacts on user satisfaction and resource utilization.  
\begin{table}
\begin{tabular}{l c c c c} \hline
**Architecture** & **MAPE** & **R\({}^{2}\)** & **Training Time (h)** & **Memory Usage (GB)** \\ \hline LSTM & 7.21\% & 0.938 & 2.3 & 4.2 \\ Transformer & 7.86\% & 0.921 & 5.7 & 8.9 \\ CNN-LSTM & 7.54\% & 0.927 & 3.8 & 6.1 \\ Attn-RNN & 8.12\% & 0.914 & 4.2 & 5.8 \\ \hline GRU & 7.89\% & 0.919 & 2.1 & 4.0 \\ \hline \end{tabular}
\end{table}
Table 2: Performance comparison of different architectures.
## 2 Integration of Carbon Capture Technologies in Ships: Status and Requirements  
Several concepts on maritime CCS technologies have been proposed and analyzed through both academic research and joint industry projects. In 2009, DNV conducted the first study to demonstrate the technical feasibility of maritime CCS [19]. The study assessed a CCS design for 90% capture of the exhaust gas of a very large crude carrier (VLCC) fueled by heavy fuel marine oil (HFO), exploring candidate technologies including chemical absorption, adsorption, and membrane separation. Because of the increased capture rate and the conventional technology features (i.e., conventional solvents and auxiliary boiler use), the energy penalty was very high and could be reduced by 25 to 30% only by using improved solvents. [PERSON] [20] considered MEA absorption on-board an 8000 ton general cargo vessel with a 3000 kW LNG fueled engine. The system achieved 87% carbon abatement with a thermal demand of 1 to 1.2 MW. [PERSON] and [PERSON] [21] evaluated the cost impact of different solvent-based CO\({}_{2}\) capture integration options in a reference cargo ship with a propulsion system consisting of two 4-stroke engines of 17 MW power capacity and 3 generators of 1 MW each. The removal rate was 73%, at the expense of 21.4% additional energy demand on-board. Further, [PERSON] et al. [22] demonstrated that amine-based CO\({}_{2}\) absorption is feasible for 2 cargo ships (1280 kW--inland ship and 3000 kW--8000 ton general cargo vessel) equipped with dual-fuel 4-stroke engines, with 60% efficiency. CCS technologies are not directly transferable from land- to sea-based applications without adaptation to the maritime environment. On-board the ship, certain technical, operational, safety, and value-chain constraints need to be satisfied, while the energy and space resources are limited. In this context, the suitability of CCS technologies for on-board use can be assessed by addressing a set of multidimensional specifications, including the following: technology maturity, compactness, endurance in saline environments, wide operability range, low energy penalty, effectiveness at low carbon content, endurance in impurities, low effect of ship motions, CO\({}_{2}\) product characteristics, health and safety performance, and capital and operational cost aspects. Among this list, a set of KPIs that can be used to assess the suitability of solvents for ship implementation was screened and is shown in Table 1.
## 3 Materials and Methods
### Image-Based Transformation Techniques
#### 3.3.1 Pixel Representation  
Pixel Representation (PR) is a straightforward approach that transforms one-dimensional numerical data into a two-dimensional image by mapping each value directly to grayscale pixel intensity [36]. This method provides a basic yet effective visualization of raw data, where each student's behavior sequence is represented as a matrix of pixel intensities that correspond to the magnitude of the data values. The grayscale level of each pixel indicates the behavior data's numerical value, with darker pixels representing higher values and lighter pixels indicating lower values. This approach maintains the sequential order of data points, allowing convolutional neural networks to detect patterns in student behavior over time based on pixel intensities and distributions.