esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 The Proposed Scheme for WSP Minimization
### Practical Deployment Limitations  
While the proposed scheme demonstrates theoretical advantages in WSP, its real-world implementation faces three critical challenges that warrant further investigation:  
Reliance on perfect CSI assumptions: Full CSI assumption is adopted in foundational works like [12; 28], where algorithms achieve theoretical performance bounds but require high-overhead feedback. Partial CSI assumption reduces feedback overhead at the cost of performance degradation. These align better with dynamic environments where the CSI changes rapidly (e.g., >100 Hz Doppler shifts). While partial CSI assumptions may degrade absolute performance metrics (e.g., 2-3 dB loss in SINR estimation), they preserve the fundamental performance trends demonstrated in most existing works. The proposed scheme's performance is contingent upon perfect CSI availability, which is challenging in dynamic wireless environments such as high-mobility scenarios. In practical deployment, the proposed scheme should integrate robust CSI estimation techniques like compressed sensing to address this challenge.  
Scalability of centralized computation: As mentioned in Section 3.2, the \(O(\max\{M,N\}^{3})\) complexity of the KM algorithm poses significant latency challenges in ultra-dense networks. A promising alternative lies in meta-heuristic optimization, which can help reduce computational complexity by limiting the number of potential connections, thereby accelerating the Kuhn-Munkres (KM) algorithm for link matching and enhancing the convergence speed of [PERSON]'s method.  
Power consumption imbalance: In battery-constrained IoT devices, the circuit power dominates total consumption, diminishing the benefits of transmission power optimization. Although our optimization focuses on transmission power, future work on holistic power models is required. Considering constant baseband processing overhead, implementing adaptive sleep scheduling could better align transmission-circuit power tradeoffs.
# An Event-Driven Serverless ETL Pipeline on AWS
## 7 Limitations and Future Work  
The discussion of this work's results highlights the capabilities of the proposed system across many aspects. In particular, we demonstrated that the proposed architecture can cope reliably with large event payloads for frequencies up to one event per second. What is more, we highlight the cost effectiveness of this serverless implementation by modeling the chargeable components of the architecture and underlining their dynamic pay-as-you- go nature, as opposed to fixed pay rates of serverful architectures. Nevertheless, we summarise the limitations of our architecture and expand on future work to address these. The main deficiency of our architecture is the SQS data throughput bottleneck. This is naturally inherited by the AWS SQS 256 KB message size restriction. Furthermore, in our methodology, we intentionally omit the pricing of components that are used for pipeline failover, such as dead letter queues. Finally, the lack of lambda worker orchestration requires that each ETL pipeline must be guaranteed in order to terminate before the execution time of the AWS lambda functions runs out.  
This research can be extended in a variety of ways. Regarding the evaluation and architecture design, there is already a list of parameters in our backlog that we pursue to tune and experiment. One of the top future work prioritize is to overcome the limitations of the proposed architecture by implementing alternatives to SQS data transfer. A possible workaround would be to keep using SQS for triggering, but routing the data through S3 or adopting an asynchronous approach for delivering SQS messages. Both of these approaches would have their pros and cons and, hence, they are at the top of the future tasks, for a more systematic investigation. In addition, in order to overcome the intrinsic limitation of execution time that is imposed by the AWS Lambda function service, we are planning to extend the proposed architecture, such that it can automatically orchestrate long running ETLs to multiple interconnected Lambda function invocations. Adding to this, improvements may be sought in the writing stage, and in terms of how the data are chunked. Adding compression or further function orchestration may potentially eliminate some of the limitations discussed in Section 6.4. Although this research provides evidence of resilience against uniform event payloads, further investigation on the limits of the architecture as frequency grows unpredictably would provide useful insights. Finally, we plan to expand our evaluation with similar architectures that are built on other major cloud platforms, such as Azure, GCP, and IBM, as comparison may allow for further conclusions to be drawn.  
Figure 6: Lambda worker costs in GB/Seconds.
# Simulating Forest Dynamics of Lowland Rainforests in Eastern Madagascar
## 3 Results
### Forest Productivity  
A time series analysis of net primary productivity (NPP), plant respiration, gross primary productivity (GPP), and tree mortality reveals an expected pattern through time (Figure 5, top). After the initial exponential increase in productivity in the first 25-30 years, there is a sharp decline in respiration and a spike in mortality, as the young forest stand undergoes initial competition and thinning. By year 60, respiration has reached a low, while GPP is peaking; NPP is highest as dominant trees quickly grow into a closed canopy forest. At this point, mortality is also on the rise due to the forest thinning and competition for resources. At about year 100, plant respiration reaches a second peak, where GPP is declining, thus NPP is the lowest during the entire simulation, and mortality is high. Following successional dynamics, growth has slowed as the canopy has closed, there is dieback of the out-competed individuals, and pioneer species are being replaced by intermediate and climax species. By approximately year 250, the forest reaches an equilibrium with respect to productivity (Figure 5, top).  
Figure 4: **(a)** Time series showing simulated biomass among the successional groups (shade intolerant, intermediate, shade tolerant) for 1000 years of simulation, as compared to Betampona forest (dots); **(b)** The same figure as on the left but comparing succession groups to Zahamena forest.  
Related to productivity, carbon flux is an important measurement used to classify the contribution of an ecosystem to the global carbon cycle. As shown in Figure 5, the net ecosystem exchange (NEE; in tons of carbon per hectare per year) indicates that the forest switches from a carbon sink to a carbon source after 75 years. During the thinning stage as described above, the forest emits carbon to a maximum of about 3.5 t Cha\({}^{-1}\) year\({}^{-1}\) in year 95. By year 130, the forest once again becomes a carbon sink, and for the remainder of the 1000 simulation, the forest fluctuates between sink and source. The NEE values are very small and range between 1 and \(-1\) t Cha\({}^{-1}\) year\({}^{-1}\), consequently the forest can be considered as a net neutral or a very slight carbon sink ecosystem from year 130 to year 1000.
## 2 Experimental Methods
### Antitumor Property and Cytocompatibility
#### 2.4.3 CCK8 Assay  
The U2 OS cells were adopted to evaluate the antitumor property. DMEM with 10% fetal bovine serum was used as blank control. U2 OS cells (2 \(\times\) 10\({}^{4}\) cells/mL) were seeded in 96-well plates and cultured with DMEM supplemented with 10% FBS for 24 h. The incubated medium was then substituted by 200 \(\upmu\)L of ZK60 or ZK60-La extracts. At 1 day, 3 days, and 5 days, the incubated medium was then removed and 200 \(\upmu\)L medium containing 10 \(\upmu\)L cell counting kit-8 (CCK-8) solution (Dojindo Laboratories, Tokyo, Japan) was introduced, followed by continuous incubation for another 2 h. Optical density (OD) values at 570 nm were obtained through a microplate reader. The relative growth rate (RGR) of cells was calculated according to the equation: RGR = (OD of the sample/OD of blank control group) \(\times\) 100%. Human embryonic kidney 293 cells (HEK 293 cells) were used to assess the cytocompatibility. The cells (1 \(\times\) 10\({}^{5}\) cells/mL) were incubated in a 96-well plate for 24 h. Then, 100 \(\upmu\)L of extracts were added to each well to replace the medium. Then, 10 \(\upmu\)L CCK-8 solution was added to each well after incubating for 1 day, 3 days, and 5 days, and subsequently incubated for another 2 h. OD values at 570 nm were obtained using a microplate reader.
# Differential Evolution with Linear Bias Reduction in Parameter Adaptation
## 2 Materials and Methods
### Proposed Approach: Linear Bias Reduction
2:\(H=5,A=0,M_{FF}=0,5,M_{CF}=0,8,r=1\dots H\)
3:\(M_{xf,1-1}=0,M_{Cz,1+1}=0,9,NA=07 NPI\) if\(\mathsf{SO}\)
4:\(g=0\)
5: Initialize population \(p=(x_{1},\dots,x_{NPI})\) randomly
6:while\(NFE<NF_{max}\)do
7:\(S_{F}=\emptyset,S_{Cz}=\emptyset,\Delta f=0\)
8:for\(i=1\) to \(NP\)do
9: Current memory index \(r=randint[1,H+1]\)
10:\(\mathrm{Cr}=randint(M_{Cz,0},1)\)
11:\(\mathrm{Cr}=min(1,max(0,Cr))\)
12:repeat
13:\(F=randint(M_{Fz,0},1)\)
14:until\(F\geq 0\)
15:\(F=min(1,F)\)
16: Get \(F_{x}\) if\(\mathsf{SO}\)
17: Limit \(\mathrm{Cr}\) if\(\mathsf{SO}\)
18:repeat
19: Randomly generate \(r_{1}\) from \(p\%\) best
20: Randomly generate \(r_{2}\) from \(1,NP_{F}\)
21: Randomly generate \(r_{3}\) from \(1,NA_{F}+NP_{F}\)
22:until\(i+r_{1}\) to \(r_{2}\
eq r_{3}\)
23:for\(i=1\) to \(D\)do
24:if\(\mathsf{SO}\)then
25:\(v_{ij}=x_{ij}+F_{\mathrm{tr}}(x_{i,1-i}-x_{ij})+F(x_{i,2j}-x_{i,j})\)
26:else
27:\(v_{ij}=x_{ij}+F(x_{i,1-i}-x_{ij})+F(x_{i,2j}-x_{i,j})\)
28:endif
29:endfor
30: Get \(u_{i}\) with \(Cr\), and calculate \(f(u_{i})\)
31:if\(f(u_{i})<f(x_{i})\)then
32:\(v_{i}\to A,x_{i}=u_{i},F\to S_{Cz},f(x_{i})-f(u_{i})\to\Delta f\)
33:endif
34:endfor
35: Update population size \(NP_{x+1}\)
36: Recalulate maximum archive size \(NA_{x+1}\)
37: Recalulate LBR \(p_{x}\) parameter
38:if\(|A|>NA_{x+1}\)then
39: Remove random individuals from the archive
40:endif
41:if\(NP_{F}>NP_{x+1}\)then
42: Remove worst individuals from the population
43:endif
44:if\(S_{F}\
eq\varnothing\) and \(S_{Cz}\
eq\varnothing\)then
45:if\(\mathsf{DISH}\)then
46: Update \(M_{Fz}\) and \(M_{Cz}\) with LBR and \(\Delta f\) as the weight
47:else
48: Update \(M_{Fz}\) and \(M_{Cz}\) with LBR and distance as the weight
49:endif
50:endif
51:\(k=k+1,k=g+1\)
52:if\(k>H\)then
53:\(k=1\)
54:endif
55:endwhile
56: Return best solution \(x_{best}\)
```  
**Algorithm 1**L-SHADE-LBR. The next section contains the experimental setup and results of the LBR application to state-of-the-art DE algorithms on the CEC 2017 and 2020 competitions benchmark problems.
## 3 Results and Discussion
### Experiment of Noise Source Separation  
#### 3.2.1 Experimental Setup in Semi-Anechoic Chamber  
In order to verify the accuracy of the noise separation technique using the phase conjugation method, it is necessary to conduct a validation experiment using sound sources that have known source information and can emit sound independently. In order to simulate the field situation of a substation, we carried out noise separation of two same-type loudspeakers in a semi-anechoic room environment. Table 1 lists the experimental equipment. All the equipment is connected according to the schematic diagram shown in Figure 7, where the source distance is \(l_{1}=0.7\) m; the distance of test points is \(L_{1}=2\) m; \(d_{1}=2\) m and \(d_{2}=-2\) m are, respectively, array-to-sources and array-to-test-points distances; the line array is composed of eight microphone elements and the array element spacing is \(\Delta=0.1\) m; and a reference microphone is located in middle position of two loudspeakers.  
The noise separation technique using phase conjugation is a method of processing and calculating noise signals in the frequency domain, which is actually a separate processing of individual frequency noise signals. Therefore, the experimental sound source emits a simple harmonic noise at a single frequency 400 Hz. The sounding procedure (MATLAB code) is:  
Fs = 44,100; T = 50; n = Fs*T; t = T*linspace(0,1,n + 1);  
A1 = 10; A2 = 20; f = 400;  
p1 = A1*sin(2*pi*ft); p2 = A2*sin(2*pi*ft); and  
p = [p1' p2']; sound(p,Fs),  
where Fs is the sampling frequency, T is the sounding time, p1 is emitted by loudspeaker A, and p2 is emitted by loudspeaker B. The microphone line array and the reference microphone measure complex sound pressure and its gradients when two loudspeakers act simultaneously, and the test microphones measure sound pressure amplitude at the three  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**No.** & **Equipment** & **Manufacturer** & **Type** & **Quantities** \\ \hline
1 & Loudspeaker & EDIFIER & - & 2 \\
2 & Microphone & B\&K & 4189 & 9 \\
3 & Microphone stand & - & - & 1 \\
4 & DAQ (data acquisition) system & B\&K & 3050-A-060 & 2 \\
5 & Wire & - & - & Certain \\
6 & Computer & - & - & 1 \\
7 & Derrick crane & - & - & 4 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Experimental equipment.  
Figure 7: Schematic diagram of experiment equipment connection.  
test points when two loudspeakers act simultaneously or individually. The experiment scene is shown in Figure 8, where 1, 2, and 3 represent test points; 4 is the line array; 5 is the reference microphone; 6 and 7 are loudspeakers; 8 is a computer; and 9 is the DAQ system.
## 3 Results
### Fault Diagnosis
#### 3.2.5 Autoregressive Linear Prediction (ALP)  
According to the common practice, the Autoregressive Linear Prediction technique is used as a pre-whitening action and it implies at first obtaining the weighting coefficients of the fourth-order autoregressive model (with a form similar to that of a IIR filter), applying Yule-Walker equations and the Levinson-Durban recursive algorithm, then computing the prediction and finally deriving the residual signal as the difference between the initial and the predicted ones.  
In the present case, ALP and SES techniques remove part of the background noise, but the improvement on the information content remains poor (Figures 28 and 29).
## 3 Hardware Speedup: Advanced Sensors and Ultra-Fast Computing Platforms
### SPAD Arrays as High-Resolution Time-Resolved Sensors
The high speed of this sensor is expected to reduce the acquisition time of quantum plenoptic images by two order of magnitudes with respect to the first CPI experiment [16], in which the region of interest on the sensor was made of \(700\times 700\) pixels for the spatial measurement side, and \(600\times 600\) for the directional measurement side; \(2\times 2\) binning on both sides during acquisition and a subsequent \(10\times 10\) binning on the directional side led to the effective spatial resolution of 350 pixels, and angular resolution of \(25\times 25\) pixels. We are also working towards several further optimizations of the sensor system, e.g., by developing gating for noise reduction in QPI devices. In order to employ the full sensor (512 \(\times\) 512 pixels), we are implementing a synchronization mechanism for a pair of imagers by means of two FPGAs, so as to operate on a common time-base at the nanosecond  
Figure 4: SwissSPAD2 photomicrograph (**left**) and pixel schematics (**right**). The pixel consists of 11 NMOS transistors, 7 with thick-oxide, and 4 with thin-oxide gate. The pixel stores a binary photon count in its memory capacitor. The in-pixel gate defines the time window, with respect to a 20 MHz external trigger signal, in which the pixel is sensitive to photons. The image is reproduced with permission from Ref. [22], copyright IEEE, 2018. Figure 3: SwissSPAD2 gate window profile. The transition times and the gate width are annotated in the figure. The gate width is user-programmable, and the minimum gate width in the internal laser trigger mode is 10.8 ns. The image is reproduced with permission from Ref. [45], copyright [PERSON], published by IOP Publishing Ltd., 2020.  
level (to this end, two control circuits shall operate from a single clock and have a direct communication link). Finally, the SwissSPAD2 arrays are being integrated with a fast communication interface in order to speed up data transfer and make it possible to deliver, in a sustained way, full binary frame sequences to a GPU. The latter will run advanced algorithms for data pre-processing, image reconstruction and optimization, as we will discuss in more detail in the next subsection.
# Towards Improved Inertial Navigation by Reducing Errors Using Deep Learning Methodology
###### Abstract
[PERSON] and [PERSON] [60] presented a new sensor fusion system with GPS, IMU, and wheel odometry by applying deep RL. The team proposed an unscented Kalman filter (UKF) which is suitable for solving nonlinear conditions. In addition, the team utilized Model Predictive Control (MPC) in the dynamic vehicle control system. The parameters of MPC were produced by the data collected from GPS, IMU, and wheel odometry, which were trained by using deep RL. The developed deep-RL-based MPC framework achieved estimated travel distance error of 0.82% and a root mean square error of 0.227m in the path tracking. It also demonstrated up to 32.64% of error improvement for the distance by using RL-based MPC compared with traditional MPC. In this work, we describe a methodology of using a deep learning algorithm that is optimized for implementation on size-, weight-, and power- (SWaP) efficient hardware for low-cost and portable systems. Specifically, the developed algorithm has a simple network structure and helps to minimize the errors of IMU signals. Compared with EKF approaches [19; 20; 21; 22], the proposed system does not need external measuring sources such as GPS or vehicle sensors. EKF approaches require external measuring sources to achieve high accuracy for the processing of IMU signals. Compared with prior PF approaches [23], we applied the method for both high-grade and low-grade IMUs. Compared with an RF [39] approach, our work can reduce the errors of IMU signals from both accelerometers and gyroscopes. In the current work, we were able to apply our algorithm with a higher data-sampling rate compared with prior ANN, CNN, or LSTM approaches [40; 45; 46; 52; 53; 54; 55; 56]. The algorithm can learn from the responses of a particular inertial sensor while subject to inherent noise errors and provide near real-time error correction. We implement a time-division method to divide the IMU data into small step sizes. By using this method, we make the IMU outputs fit the input format of CNN. We set a total of 121 levels of accelerations from accelerometers (operational to \(\pm\) 3 m/s\({}^{2}\)) and 91 levels of angular velocities from gyroscopes (operational to \(\pm\)45\({}^{\circ}\)/S) as outputs to train CNN. Raw datasets are collected from various grades of IMUs and several IMUs of the same grade. We configure three data sizes for the input formations of the networks to examine the performances of CNN for various performance grades of IMUs and IMUs of the same type but different manufacturing batches. The primary objective of this methodology is developing algorithms with higher performance and lower complexity that would allow implementation on ultra-low power artificial intelligence microcontrollers such as the Analog Devices MAX78000.
## 3 Numerical Analysis
### Mesh and Boundary Conditions  
The general geometry of the model consists of 15-node triangular elements. To assess the accuracy of the analysis, various finite element meshes were generated, including very fine, fine, medium, and coarse elements. It was determined that a medium mesh would suffice. The finite element network created for one-half of the model comprises 1864 elements and 15,487 nodes. Given the symmetry of the two-dimensional model, it was considered adequate to model only half of it to reduce computation and the number of elements required. The actual boundary conditions in the model tests were reflected in the analyses. Since the rigid tank walls and rigid tank bottom in the model tests restricted the horizontal movement of the system, the lateral movement of the model system was fixed. As a result, the system was allowed to move freely in the loading direction and was fully fixed in the opposite direction.
## 4 Simulations of Tightening, Vibration-Induced Loosening, and Fatigue
### Vibration-Induced Loosening  
It has been validated that bolted joints tend to loosen under cyclic transversal vibration and that loosening results in the loss of preload. In this study, the process of the preload decay under cyclic transversal vibration was simulated via FEA with the purpose of evaluating the anti-loosening ability of different kinds of bolted joints. [PERSON] et al. [1] investigated the change in preload with an increase in vibration cycles under different vibration frequencies through FEA and experimentation. It was proven that the vibration frequency had no effect on the loosening behavior. Since the experimental vibration frequency was less than 12.5 Hz [44], the cyclic transversal vibration of the movable plate was assumed to be a quasi-static process. Based on the quasi-static assumption, cyclic transversal vibration was applied to all nodes on the outer cylindrical surface of the movable plate, as shown in Figure 11. The vibration-induced loosening simulation process of all bolted joints was carried out under an initial preload of 30 \(\pm\) 1 kN. The vibration amplitude was set to 0.5 mm [39] along the \(x+\) and \(x-\) directions, and vibration was performed with a total of 30 cycles.  
Figure 11: Exertion process of cyclic transversal vibration within a complete cycle.  
Figure 10: Comparison of the torque–tension relationships obtained by FEA and Equation (1).  
After the simulation of tightening and cyclic vibration was completed, the stress distributions of the bolt thread and bearing surface for a regular bolted joint were extracted, as shown in Figure 12. It can be seen that the position of maximum stress in the bolt thread root moved upward after cyclic transversal vibration, which indicates that rotation loosening occurred. Additionally, the stress on the bearing surface of the movable plate decreased obviously after vibration.  
In order to verify the influence of FE mesh density on the result, the anti-loosening bolted connections were meshed with different densities (the element size was small), as shown in Figure 13. The preload variations under cyclic transversal vibration in different mesh densities are depicted in Figure 14. It can be seen that with the improvement in mesh refinement, the FEA results tend to be consistent. However, it is evident that an increase in mesh density significantly reduces the solution efficiency of FE simulation. Thus, considering the achieved accuracy and efficiency, the choice of the FEA elements (type 1) reported in this manuscript was reasonable.  
Figure 12: Stress distribution of the regular bolted joint (**a**) after tightening and (**b**) after vibration.  
Figure 13: Regular bolted joint meshed with different densities.