esa-sceva/satcom-chunk-collection huggingface dataset:
# A Low-Latency Fair-Arbiter Architecture for Network-on-Chip Switches
## 1 Introduction  
As the feature size of chips is reduced to the nanometer level, more processing elements can be placed on a system-on-chip (SoC) [1]. In recent years, AMD-Xilinx has proposed a SoC named the adaptable computing acceleration platform (ACAP) [2], which is a device-global memory-mapped network-on-chip [3, 4] that connects the components and fabric in an integrated fashion. As the NoC unifies communication between the processor system, FPGA fabric, memory subsystem and other hardened accelerator functions, it is widely used in many complex systems, such as multi-core processing chips and large systems-on-chip. As an important scheduling part in NoC, an arbiter is essential to provide fair and reasonable services for shared resources, especially in high-speed network-on-chip switches [5].  
NoC switches generally consist of input ports, a schedule, a crossbar and output ports, as shown in Figure 1. The round-robin arbiter (RRA) as a schedule is widely used in the NoC switching system [6, 7]. It aims to provide control signals to the crossbar switch fabric. The RRA is placed on each output port to ensure that each input port can potentially request connections to all output ports. When the crossbar receives the arbitration information from the RRA, it opens the corresponding channel from the ingress to the egress. Thus, a packet is transferred. Therefore, the arbiter must be fair to prevent port starvation. However, the traditional switch is prone to a throughput limit because of the head-of-line (HoL) blocking [8] phenomenon. There are many solutions to the HoL problem [9]. The two most commonly used methods are virtual output queuing (VOQ) [10] and virtual channels (VCs) [11, 12]. Although VOQ and VC can deliver better performance, they are challengingto implement because both of them require a high clock frequency [13]. As the arbiter is located in the critical pipeline stage [14] of the switch systems, the critical path of the arbiter limits the performance of the system. Therefore, designers should focus on the low latency and fairness of the arbiter.  
In this study, we propose a new fair and fast switch arbiter called FSA. In FSA, the grant signal is used for priority updates to ensure the fairness of the leaf node. Moreover, to avoid the wrong update of the priority in the upper level, we propose using the lock signal to modify the request passing to the upper level. Furthermore, low latency can be guaranteed by using the priority logic in parallel.  
This paper is organized as follows: Section 2 lists some typical arbiters and analyzes their advantages and disadvantages. Section 3 describes the structure of the FSA and demonstrates its fairness and high performance. Section 4 provides arbitration observation experiment results for all arbiters. Finally, we conclude this paper in Section 5.
## 6 Conclusions  
Modern wearable sensor systems require the processing and transmission of more and more data. Energy and teletransmission aspects should be taken into account when designing such solutions. The necessity to transmit large amounts of data, as in the case of the presented sensor system for hand motion acquisition, is related to the load on the radio module and external teletransmission infrastructure. The process of radio data transmission is also a significant energy burden for modern wearable battery-powered sensor systems. The article presents the algorithm that was implemented in the sensor system and then tested. This algorithm controls data transmission via the Wi-Fi module. Depending on the dynamics of hand motion, the algorithm changes the number of transmitted frames and the length of these frames, which allows to reduce the energy demand of the radio module, but also significantly reduces the load on the Wi-Fi network. This data transmission procedure does not degrade the image quality of the monitored signal. The conducted research shows that new algorithms for data processing and transmission used in sensor systems allow for the improvement of energy (battery operation time) and teletransmission parameters (lower network load). The developed adaptive algorithm can be used to monitor motion with different dynamics, as in the case of gestures, where there is a movement phase (increased amount of transmitted data) and a rest phase (reduced amount of transmitted data).
# Equivalence between LC-CRF and HMM, and Discriminative Computing of HMM-Based MPM and MAP
## 4 Discriminative Classifiers in Generative HMMs  
One of the interests of HMMs and some CRFs with hidden discrete finite data lies in possibilities of analytic fast computation of Bayesian classifiers. As examples of classic Bayesian classifiers, let us consider MPM (Equation (4)) and MAP (Equation (5)). However, in some domains such as NLP, CRFs are preferred to HMMs for the following reasons. As HMM is a generative model, MPM and MAP used in HMM are also called \"generative\", and people consider that HMM-based MPM and MAP require the knowledge of \(p(y_{n}|x_{n})\). Then people consider it as improper to use the HMM-based MPM and MAP in situations where the distributions \(p(y_{n}|x_{n})\) are hard to handle. We show that this reason is not valid. More precisely, we show two points:  
1. First, we notice that regardless of the distribution \(p(x_{1:N},y_{1:N})\), all Bayesian classifiers are independent from \(p(y_{1:N})\), so that the distinction between << generative >> and << discriminative >> classifiers is misleading: they are all discriminative;
2. Second, we show \"discriminative\" computation of MPM and MAP in HMMs is not intrinsic to HMMs but is due to its particular classic parameterization Equation (1). In other words, changing the parametrization, it is possible to compute the HMM-based MPM and MAP without using \(p(y_{1:N}|x_{1:N})\) or \(p(y_{1:N})\).  
The first point is rather immediate: we note that Bayesian classifier \(g_{L}\) is defined by a loss function \(L:\Omega^{2}\to R^{+}\) through  
\[[g_{L}(y_{1:N})=\hat{\alpha}_{1:N}]\Longleftrightarrow[E[L(g_{L}(y_{1:N}),X_ {1:N})|y_{1:N}]=\underset{x_{1:N}}{inf}E[L(x_{1:N},X_{1:N})|y_{1:N}]] \tag{20}\]  
it is thus immediate to notice that \(g_{L}(y_{1:N})\) only depends on \(p(x_{1:N}|y_{1:N})\). This implies that it is the same in a generative model or its equivalent (within the meaning of Definition 1) discriminative model.  
We show (ii) by separately considering the MPM and MAP cases.
###### Abstract  
Thermal printing is a prevalent method due to its advantages of rapid output, cost effectiveness, and ease of use. However, the quality of thermal printing is influenced by the printing speed, the temperature, and the concentration and characteristics of the materials. This research employs a BP neural network to forecast print quality, utilizing two activation functions. The findings indicate that a dual-layer hidden configuration utilizing the GeLU activation function yields a lower root-mean-square error (RMSE). The optimal configuration identified consists of six neurons in the first hidden layer and three neurons in the second hidden layer. To enhance the predictive performance, a PSO algorithm was integrated with the PSO-BP model to refine the parameter selection, which included ambient temperature, printing speed, and printing concentration, with iterative training and validation conducted via the gradient descent algorithm. The PSO-BP network achieved an MAE of 0.1108, an RMSE of 0.145, an MSE of 0.021, and an R\({}^{2}\) value of 0.9916 in predicting print quality. These results substantiate the stability and reliability of the neural network model developed with the PSO algorithm. Further validation with ten sets of test samples demonstrated that the model attained an average absolute error of 2.77% in print quality predictions, indicating robust generalization capabilities and precise forecasting.  
Keywords:thermal printing; quality prediction; optimization; BP neural network; particle swarm optimization (PSO) +
Footnote †: journal: Applied Sciences
## 2 Related Work
The UL SE for two practical processing strategies, i.e., the fully centralized processing approach with global MMSE or MR combining and the large-scale fading decoding (LSFD) technique with local MMSE or MR combining, is examined. The MMSE and MR combining are studied in [14; 21], but the number of antennas per user equipment considered is one, which is not practical. To improve the SE effectiveness of the system, the author proposed an effective UL precoding method that relies only on the UE-side correlation matrices' eigenvalues. Additionally, the closed-form SE equations for the LSFD scheme with MR combining have been developed. The channel estimation used in this work has high computational complexity. The analysis is only based on the SE; the EE and area throughput are not addressed.
# Comparison of the Sensitivity of Various Fibers in Distributed Acoustic Sensing
## 3 Results  
Testing began with \"Fiber 1\", which was installed in the setup, and measurements were conducted with the speaker emitting the wideband signal described earlier, followed by a \"silence\" measurement with the speaker off to capture background noise. Each subsequent fiber segment was installed and measured with the speaker on. The results are presented in Figure 7. To enhance clarity, moving averaging with a 5-point window was applied to the curves in Figure 7b. Thus, the averaging window was being translated along the frequency domain of the obtained frequency-dependent acoustic sensitivity for each FUT.  
Figure 6: (**a**) Results of two consecutive measurements for the same FUT, showing consistency in the detected signal; (**b**) error bars for these curves, indicating measurement variability and reliability.  
The results indicate that \"Fiber 5\" demonstrates the highest acoustic sensitivity across signal frequencies between 100 and 4500 Hz, after which it is surpassed by \"Fiber 7\". Although \"Fiber 7\" shows average sensitivity below 4500 Hz--lower than that of \"Fiber 1\" and nearly equivalent to \"Fiber 3\"--it exhibits relatively stable, frequency-independent sensitivity across the entire range, a trait unique among the tested fibers. For most fibers, sensitivity differences become negligible at acoustic frequencies above 5000 Hz, with \"Fiber 1\".  
Figure 7: Acoustic sensitivity test results for all fiber samples. (**a**) Raw data showing initial sensitivity measurements across samples; (**b**) smoothed data with a 5-point moving average applied, highlighting trends in frequency-dependent sensitivity for each fiber.  
7\" and \"Fiber 5\" being exceptions. \"Fiber 4\" displays good sensitivity below 2500 Hz, but then decreases to match \"Fiber 1\".  
\"Fiber 2\" demonstrates notable sensitivity within the 100 to 1500 Hz range but experiences a rapid decline afterward, becoming the least sensitive and the first to reach background noise levels. \"Fiber 6\" exhibited sensitivity comparable to that of \"Fiber 1\", though this was slightly lower below 3000 Hz. \"Fiber 3\" displayed sensitivity similar to \"Fiber 7\" but showed a decrease below 4000 Hz.
## 4 Conclusions  
This paper measured and evaluated the UWOC system in different types of seawater salinity alongside simultaneous environmental factors, such as turbulence and water flow, using a 450 nm blue-light LD. The saline water was measured at 22.78 g/L, 37.96 g/L, and 45.56 g/L by adding sea salt into the water tank. The 45.56 g/L saline water could reach a maximum bit error rate (BER) of 4.851 \(\times\) 10\({}^{-6}\) at \(-\)7.41 dBm at a 3 m distance. In 45.56 g/L saline water, the optical power decreased by about 80.22% more than in tap water. The UWOC system was also evaluated in various water temperatures ranging from 15 \({}^{\circ}\)C to room temperature (Typ. 26 \({}^{\circ}\)C) until 30 \({}^{\circ}\)C. As the temperature increased to 30 \({}^{\circ}\)C, the BER degraded to 5.12 \(\times\) 10\({}^{-6}\). The water temperature was non-uniform, causing a different refractive index and higher attenuation. Water flow was generated in the various types of water salinity to compare simultaneous environmental effects in the UWOC system. In the 45.56 g/L saline water with water flow, the UWOC system was still capable of reaching a BER of 4 \(\times\) 10\({}^{-4}\). The seawater salinity contributed to the UWOC system's attenuation and degraded the signal. The differences between the simulation and experiment for UOWC were compared in this study, in conjunction with the results comparing salinity in seawater with various densities.  
Methodology, S.A. and S.-K.L.; validation, P.-J.L. and C.-H.Y.; experiment, S.A., A.W., and Y.-L.L.; data curation, A.W.; writing--original draft preparation, S.A. and Y.-L.L.; writing--review and editing, S.-K.L. and O.H. All authors have read and agreed to the published version of the manuscript.  
The work is supported by the National Science and Technology Council, Taiwan, under Grant NSTC 110-2224-E-011-004, NSTC 110-2221-E-011-109, and NSTC 111-2224-E-006-008. This work is also supported by the joint project under Grant Kyutech-NTUST-111-05.  
Not applicable.  
The authors would like to thank [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON] for their kind help and support.  
The authors declare no conflict of interest.  
Figure 11: Channel estimation for each water flow measurement.
# Awareness to Action: Student Knowledge of and Responses to an Early Alert System  
[PERSON]  
1 Learning Engineering Institute, Arizona State University, Tempe, AZ 85281, USA; [EMAIL_ADDRESS] (M.G.); [EMAIL_ADDRESS] (M.W.); [EMAIL_ADDRESS] (J.G.); [EMAIL_ADDRESS] (D.N.C.); [EMAIL_ADDRESS] (R.D.R.); [EMAIL_ADDRESS] (T.A.); sm  
connect that student with resources or student success staff proactively (e.g., by prompting an academic advisor to reach out to the student) [9; 10; 11; 12]. This institutional outreach is essential within the theoretical framework provided by [PERSON] [13], as faculty interactions forward a students' social integration into campus and thus decrease the risk of institutional departure.  
Early alert systems are prevalent among public and private universities in the United States. A 2014 report estimated that 85-90% of higher education institutions in the United States had an early alert system in use [14]. Early alert systems have been found to be effective in global universities as well. For example, early alert systems have been found to enhance student success at universities in Europe [15], Africa [16], and Asia [17], and Oceana [18], either by accurate detection of at-risk students or connection of students to tutoring resources.  
Early alert systems are particularly relevant to the support ecosystem at colleges and universities because they can serve as an entry point for a student to access relevant resources. For example, an early alert can prompt a student to meet with an academic advisor, who then may suggest making an appointment with the tutoring center. Thus, although early alert systems themselves may not provide help in the form of homework support or financial aid adjustment, they can prompt awareness and action that lead the student to interface with university supports that do offers those services. Though these systems interface with instructors and student success staff, the key user is the student, who decides how to respond to the alert (if at all). Thus, the current study was contextualized within frameworks of student persistence in higher education such as [PERSON]'s model of institutional departure [13] and self-determination theory [8]. Both theories highlight that students who are connected to the people and resources of an institution are more likely to persist through university.  
We sought to understand the student experience of a particular early alert system, called Academic Status Reports (ASRs). Thus, we administered a survey to undergraduate students at a large public university to examine their knowledge of and responses to ASRs. Our primary aim was to gain an improved understanding of how college students think about and react to this type of institutional support. This aim led us to develop three research questions for this study: (1) What do students know and believe about ASRs? (2) How do students respond to receiving an ASR? and (3) What recommendations do students have to improve the system?
## 6 Conclusions  
This paper proposes a novel goal-based multi-agent hierarchical model to solve large-scale TSC problems. We first divide the environment into disjoint regions, each containing several traffic lights. We abstract a manager as the upper agent in the area, and the actual traffic light as the lower agent called a worker. The purpose of the manager is to assign a long-term goal to workers in different locations by observing the environment. The worker will make actual action decisions based on the state of the environment and the goal received. In the decision-making process, the worker will fully consider the situation of the neighbor agents, and infer the neighbor's actions through its policy and the neighbor's goal. This not only solves the instability problem but also realizes the cooperation between the agents. Based on such an architecture, we can solve large-scale TSC problems by splicing regions.  
We built a symmetrical mesh environment and an actual Monaco city environment in SUMO. The experimental results, respectively, verify the effectiveness of our algorithm and the excellent performance compared to SOTA in the real world. At the same time, the TSC problem can be abstracted as a flow control problem. Therefore, the algorithm can also be applied to network traffic [34], power dispatch [35; 36], energy pipeline transportation [37], and so on.  
Conceptualization, Y.Y., P.Z. and T.G.; Formal analysis, P.Z.; Investigation, P.Z. and T.G.; Methodology, P.Z.; Project administration, Y.Y.; Resources, P.Z.; Software, P.Z.; Supervision, T.G.; Validation, P.Z.; Visualization, P.Z.; Writing--original draft, P.Z.; Writing--review & editing, Y.Y. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} RL & Reinforcement learning \\ TSC & Traffic signal control \\ GMHM & Goal-based multi-agent hierarchical model \\ MADDPG & Multi-agent deep deterministic policy gradient \\ SUMO & Simulation of urban mobility \\ SOTA & State of the art \\ DRL & Deep reinforcement learning \\ MDP & Markov decision process \\ MARL & Multi-agent reinforcement learning \\ MA2C & Multi-agent advantage actor critic \\ FMA2C & Feudal multi-agent actor-critic \\ DQN & Deep Q-network \\ DDPG & Deep deterministic policy gradient \\ LSTM & Long short-term memory \\ \end{tabular}
# Computer Tools to Analyze Lung CT Changes after Radiotherapy
## 4 Lung Images' Segmentation and Registration  
### Segmentation Process  
In order to extract the examined region of interest (ROI) areas from whole images, the segmentation process is necessary. According to [16], lung segmentation may be challenging, because there are differences in pulmonary inflation, which can lead to large variability in volumes and margins when trying to implement automatic algorithms. There is still no universal segmentation method that will properly work for all lung pathological conditions. The amount of unique lung disease cases makes accurate segmentation without human verification very difficult. We can group traditional segmentation methods into fourmain categories: thresholding-based, region-based, shape-based, and neighboring anatomy-guided [16]. We will focus on the first two approaches, i.e., thresholding-based and region-based, which were used in our system. It is important to note that usually to achieve a properly segmented image, multiple techniques are combined.
# Unmanned Aerial Vehicle Based Wireless Sensor Network for Marine-Coastal Environment Monitoring
## 3 SIMMA System Architecture
### UAV Design  
The UAV is based on a Delta Wing design, built with a foam frame with two carbon fiber rods across the wings for reinforcement and with a wingspan of 2 m. This type of design is ideal for remote areas, as it does not require a specific landing area, due to its belly pan landing system, and it is light enough (4.2 kg with battery and electronics) to be launched by hand or a small launchpad (Figure 11).  
Figure 10: Upper part of the buoy where the electronics are housed and the external sensors are fixed.  
The UAV is equipped with a Panda II autopilot system kit [51], which includes a GPS receiver module, three-axis MEMS gyros, three-axis accelerometer, three-axis magnetic sensor, a barometric pressure sensor, and an airspeed meter. This system provides a high precision flight control with three different operation modes: manual, AFSS activated (active stabilization), and autopilot. The autopilot can be controlled and configured with the use of the GCS software provided by the manufacturers. This software provides a graphical interface which provides real-time data for various flight parameters of the UAV and an electronic map for waypoint navigation. To communicate the GCS with the UAV, an FY-605 wireless data radio at 462.125 MHz is utilized [52], providing an extended transmission range of up to 15 km. Moreover, the UAV has a 900 MHz First Person View (FPV) video transmitter, with its corresponding integrated camera, and a UHF Long Range System (LRS) receiver at 455 MHz. These last two wireless modules are used to control the UAV in manual mode with a radio controller.  
Taking into consideration the UAV's battery weight, servos, and electronic systems previously mentioned, the aircraft is able to carry approximately 600 g of payload. Given that the master node, its housing, and battery pack weigh roughly 360 g, the UAV is able to fly a maximum of 40 min, with the possibility of adding further peripherals. It is worth mentioning that this device was also provided by the CICESE Institute.
# Integrated Carbonate-Based CO\({}_{2}\) Capture--Biofixation through Cyanobacteria
## 1 Introduction
While prokaryotic cyanobacteria can thrive under high pH conditions, they may not be as versatile or suitable for broader industrial applications as eukaryotic green algae. Although obtaining ideal green algae from extreme environments such as Soda Lake is a possibility, implementing alkaline cultivation as a universal microalgae culture method remains challenging due to the unique requirements and limitations associated with such microorganisms. Therefore, further research should focus on evaluating microorganisms and identifying optimal growth conditions. This is essential to enable the overall techno-economic assessment of a microalgae-based ICCU system, even before considering scaling up. After conducting an initial screening of available strains, including intentionally acquired strains such as _Eulabothece_ ZM001 [15], it was found that the \"regular\" _Spirulina_ species exhibits high potential for the intended application [20; 21; 22]. Spirulina sp. boasts the highest worldwide production volume of microalgal biomass and is renowned for its alkaliophilic nature, alongside its substantial protein content and a variety of other valuable byproducts [23]. The industrial readiness of these strains prompted us to conduct a performance assessment of two Spirulina strains, a type of blue-green algae cyanobacteria, within a laboratory-scale integrated photobioreactor. Employing a Design of Experiments (DoE) statistical approach, the simultaneous variation of three independent growth parameters such as temperature, light intensity, and bicarbonate concentration was studied. While conducting the evaluation, we also considered the outgassing of CO\({}_{2}\) into the atmosphere, which led us to explore the implementation of a closed-loop design. This design modification resulted in significantly improved CO\({}_{2}\) utilization efficiency.