esa-sceva/satcom-chunk-collection huggingface dataset:
# A Survey on Hardware Accelerators for Large Language Models
## 1 Introduction
Until now there has been no comprehensive survey on the hardware accelerators to speed up the most computationally intensive tasks of Transformers. In [1], a survey was presented on the hardware acceleration of Transformer networks for autonomous driving. The paper presented several efforts on the acceleration of tasks such as object detection, 3D segmentation, and lane detection. In 2022, [PERSON] et al. presented a survey on hardware acceleration for Transformers [2]. The paper mostly focused on the Transformer model compression algorithm based on the hardware accelerator and was limited mostly to FPGA-based implementation. In 2023, [PERSON] et al. [3] presented a comprehensive performance study of LLMs on several computing platforms and evaluated the performance characteristics of these models. The authors evaluated these systems using micro-benchmarks, a GPT-2 model, and an LLM-driven science use case, called GenSLM. For the evaluation, they used GPUs and other AI accelerators such as Sambanova, Cerebras, Graphcore, and Habana Gaudi 2. Specifically, they used LLM applications to evaluate the performances of the systems and categorized the proposed solution for the acceleration of the LLM applications. In this paper, we present a comprehensive survey of various research efforts that have aimed to accelerate Transformer networks for large language models and NLP using hardware accelerators. This survey outlines the proposed frameworks and then provides a qualitative and quantitative comparison of each, focusing on the technology, processing platform (GPU, FPGA, in-memory computing, ASIC), speedup, and energy efficiency of each framework.
# Opportunities and Challenges for Error Correction Scheme for Wireless Body Area Network--A Survey
## 3 IEEE 802.15.6 WBAN Standard  
The IEEE 802.15.6 standard defines one MAC and supports three PHYs: NarrowBand (NB), human body communications (HBC), and Ultra-Wide Band (UWB) layers [30,31]. Figure 3 illustrates the IEEE 802.15.6 standard MAC and the physical layers model. The choice of physical layer in WBAN depends on the application and its requirement. NB-PHY supports various frequency bands, and WBAN devices must be able to support transmission on at least one of those frequency bands. The details of transmission parameters for NB-PHY, including frequency band, modulation, symbol rate, and ECC found at [30]. HBC-PHY uses Electric Field Communication (EFC) technology and the band of operation centered at 21 MHz. Transmission at this layer was performed using digital circuits and electrode. That makes the receiver movement easy and reduces power consumption at the receiver.  
UWB-PHY is designed for high performance, low complexity, and low power consumption as compared with NB-PHY and HBC-PHY. This physical layer offers Impulse Radio UWB (IR-UWB) and wideband Frequency Modulation UWB (FM-UWB) technologies. In addition, UWB-PHY defines two  
Figure 3: IEEE 802.15.6 Media Access (MAC) and Physical (PHY) layer model.  
modes of operations, called default mode and high QoS mode. The default mode used in medical and non-medical applications, where the particular QoS is not well-defined. The high QoS mode is used for high-priority medical data or network control management data. Table 1 illustrates the user priority for different types of data [30]. The standard have not been tailored to ensure the reliability for each user priority and the handling of multiple user priority.  
In IEEE 802.15.6, the BCH [17; 18] code is adopted as an ECC. In NB-PHY, BCH (63, 51), and its shortened version BCH (31, 19) is used for payload and header, respectively. The modulation for narrowband PHY can be varied among Differential Binary Phase Shift Keying (DBPSK), Differential Quadrature Phase Shift Keying (DQPSK), Differential eight-Phase Shift Keying (DQPSK), and Gaussian Minimum Shift keying (GMSK) to support various data rate for payload and frequency bands.  
In UW-PHY, BCH (63, 51) is adopted as a FEC for the default mode, whereas shortened BCH (126, 63) with the HARQ mechanism is adopted as ECSs for high QoS mode. Table 2 illustrates the summary of modulation, FEC, and use of HARQ in default and high QoS modes in UWB-PHY. For the detailed operation and features of MAC layer and all PHY layers refer to the standard [30]. The error correction on WBAN communication can be performed at either PHY or MAC or both layers. In WBAN, there are different types of nodes and those nodes may be inside, outside, or on the human body. Therefore, the different types of channel models are introduced to the model channel between nodes and networks to represent the overall WBAN.
## 3 Results and Discussion
### Studies Involving Post-Stroke Individuals
#### 3.3.1 Cybersickness Incidents  
Three patients tolerated the therapy very well. Two patients experienced dizziness during the therapy. According to studies, the duration of exposure to VR experiences can increase discomfort in a proportional way [54]; therefore, the first step was to reduce the duration of therapy. In one patient, shortening the time eliminated the cybersickness symptoms he was experiencing, so the patient continued therapy. The other patient continued to experience dizziness and nausea after the reduction in therapy time, which was the reason for discontinuing therapy.
## 6 Verification
### Experimental Setup
#### 6.1.5 External Disturbances  
External disturbances may be introduced into a system for a variety of reasons. We list possible scenarios in which disturbances may occur in real-systems, as shown in Figure 5:  
* For collaborative jobs (such as kitting in warehouses; see the description in Section 6.1.1), humans may experience monotony leading to boredom and loss of attention. In such cases, it is possible for a human co-worker to accidentally collide with the robot manipulator or alter the environment in unexpected ways.
* The user may also accidentally collide or unintentionally move packaging objects in ways a robot may not anticipate. Such variations in the world may lead to tool collisions.
* Picked objects may slip from a robot's gripper if the grasp is not optimal; or if upon motion, inertial forces acting on the object cause slippage that breaks the grasp.
* Object slips may also be caused due to human collisions. Such phenomena depict a chain of anomaly reactions where one anomaly leads to another.
* The robot may also collide with packaging box during kitting.  
Figure 4: A kitting experiment consists of 6 skills (in various colors) that were modeled by DMP, respectively. Objects that need to be packaged are placed by a human collaborator before the robot in a collection bin. The shared workspace affords possibilities for accidental contact and unexpected alteration of the environment. The robot is tasked to pick each one of the objects and place them in the packaging box to its right. The visualization module uses the ALVAR tags to provide a consistent global pose with respect to the base of the robot. Ten nominal executions and one snapshot of each skill are shown.  
* Missed grasps (where the robot pinches air) are possible when the target object is moved without the robot noticing.
* Additionally, it is possible to suffer from false positives from the anomaly detector. False positives may occur for numerous reasons: system error, unreachable objects, unfeasible inverse kinematic solutions, unidentifiable objects from the visual system, to name a few.  
The seven types of anomalies we just described will be declared more succinctly in the rest of the paper as: Human Collisions (HC), Tool Collisions (TC), Object Slips (OS), Human Collision with Object (HCO), Wall Collision (WC), No Object (NO), and Other (OTHER). In Section 3.2, the introspection methodology is used to model robot skills, followed by a presentation of anomaly detection in Section 4 and anomaly classification in Section 5.
# Estimating Critical Latency Affecting Ship's Collision in Re-Mote Maneuvering of Autonomous Ships
## 4 Results
### Critical Latency Estimation and Error Analysis Results
#### 4.2.1 Critical Latency Estimation Results  
The estimation results of critical latency that could result in a collision are shown in both Tables 3 and 4.  
Table 3 shows the average value and standard deviation (SD) of critical latency for a combination of four scenarios and seven different rudder angles. The maximum and minimum average values were 8.53 min (SD, 6.53) and 0.2 min (SD, 0.11), respectively. In Scenario 1 with the encountering angle of 1\({}^{\circ}\), the average value was within 1 min, while in Scenario 4 with the encountering angle of 135\({}^{\circ}\), the average value was in the range of 4.12-8.53 min. Table 4 shows the average value and standard deviation (SD) of critical latency for combinations of four scenarios and six vessel's different speeds. The maximum average value and minimum average value were 12.74 min (SD, 3.9) and 0.12 min (SD, 0.02), respectively. In Scenario 1, the average value was within 1 min; whereas, in Scenario 4, the average value was in the range of 2.15-12.74 min.  
Figure 9 explains the average value of the critical latency for a combination of seven rudder angles and six vessel speeds, and Figure 9a-d shows the estimated critical latency for Scenarios 1-4, respectively. In all four scenarios, the average value of the critical latency is shown to gradually decrease as the rudder angle increases and as the vessel speed increases.  
In the above results, the ship scale (length, width) was not considered. With the scale considered, the critical latency values may be expected to differ. In this regard, we will discuss firstly in Section 5 (Discussion). In addition, as turning circles were derived by ship maneuvering simulation with a vessel model (cargo vessel, length 171.8 m) applied, turning circles with an actual vessel applied may be different. In this regard, we will discuss secondly in Section 5 (Discussion).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Speed 1**} & **Scenario 1** & **Scenario 2** & **Scenario 3** & **Scenario 4** \\ \cline{2-5}  & **Mean (SD 2)** & **Mean (SD)** & **Mean (SD)** & **Mean (SD)** \\ \hline \(\
u_{1}\) & 0.52 (0.16) & 4.44 (1.74) & 8.46 (2.86) & 12.74 (3.90) \\ \(\
u_{2}\) & 0.28 (0.08) & 2.24 (0.86) & 4.25 (1.42) & 6.39 (1.94) \\ \(\
u_{3}
# Surface Defect Detection Methods for Industrial Products: A Review
## 4 The Key Problems
### Unbalanced Sample Identification Problem  
The identification of unbalanced samples [97; 98] is another difficulty based on the deep learning method when used in the surface defect detection of industrial products. In deep learning, when the model is trained, it is usually required that the number of samples of various categories in the sample set be balanced. However, this ideal situation rarely occurs in reality. In more cases, the data of \"normal\" samples in the data set is usually the majority, while the data amount of \"defect\" or \"abnormal\" samples only accounts for a small part in the total sample. This phenomenon is called an \"unbalanced sample\" phenomenon. The problem of unbalanced sample identification mainly exists in the task of supervised learning. The emergence of this problem will cause the algorithm to pay more attention to the category with larger data volume and underestimate the category with smaller data volume, thus affecting the generalization and prediction ability of the model in the test data.  
At present, the identification of unbalanced samples can be dealt with from four aspects: data level, model level, feature level, and evaluation metric level.
## 4 Results and Discussion
### Recognition Result
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Sets** & **Class** & **BPNN** & **SVM** & **LDA** & **KNN** & **DT** & **Aver.** \\ \hline \multirow{4}{*}{A} & Healthy & **99.28** & **99.75** & **96.48** & **91.71** & **99.62** & **97.37** \\  & Inter-ictal & 0.69 & 0.25 & 3.52 & 6.41 & 0.38 & 2.250 \\  & Epileptic & 0.03 & 0.00 & 0.00 & 1.88 & 0.00 & 0.382 \\ \hline \multirow{4}{*}{B} & Healthy & **99.59** & **100.0** & **98.81** & **95.67** & **99.94** & **98.80** \\  & Inter-ictal & 0.35 & 0.00 & 1.19 & 2.51 & 0.06 & 0.822 \\  & Epileptic & 0.06 & 0.00 & 0.00 & 1.82 & 0.00 & 0.376 \\ \hline \multirow{4}{*}{C} & Healthy & 1.16 & 0.13 & 0.50 & 0.13 & 1.88 & 0.760 \\  & Inter-ictal & **98.81** & **99.87** & **99.50** & **99.69** & **98.12** & **99.22** \\  & Epileptic & 0.03 & 0.00 & 0.00 & 0.19 & 0.00 & 0.044 \\ \hline \multirow{4}{*}{D} & Healthy & 0.81 & 0.25 & 0.63 & 0.44 & 1.63 & 0.752 \\  & Inter-ictal & **99.15** & **99.75** & **99.37** & **99.56** & **98.37** & **99.24** \\  & Epileptic & 0.04 & 0.00 & 0.00 & 0.00 & 0.00 & 0.008 \\ \hline \multirow{4}{*}{E} & Healthy & 0.16 & 0.00 & 0.06 & 0.06 & 1.01 & 0.258 \\  & Inter-ictal & 0.06 & 0.00 & 0.06 & 0.06 & 0.06 & 0.048 \\ \cline{1-1}  & Epileptic & **99.78** & **100** & **99.88** & **99.88** & **98.93** & **99.69** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Classification results of the five classifiers on all five data-sets of Case 3 (Back-Propagation Neural Network (BPNN), Support Vector Machine (SVM), Linear Discriminant Analysis (LDA), K-nearest neighbour (KNN), Decision Tree (DT), average value (Aver.)). From the results in Tables 3-6, an interesting phenomenon revealed that datasets C and D always contained some healthy EEG components, which lowered the classification accuracy in Tables 3, 5 and 6, and increased the classification accuracy in Table 4. One possible explanation is that the structures of brain cells have a few common features, which imparts the EEG segment with some commonalities with healthy EEG segments when the motivation signals through these neurons. Similarly, the epileptic EEG segments also have some common features to those of healthy EEG segments, as shown in Tables 3, 5 and 6. In order to eliminate the influence of the number of training samples on the recognition result, the majority of the results in Tables 2-6 were obtained using 7:3 as the TTTR. In order to test the robustness of the computational performance with varying TTTRs, Table 7 provides the recognition results obtained with the BPNN classifier. As can be seen in Table 7, the results obtained by this proposed method do not significantly rely on a high TTTR.
## 1 Introduction and Review of Existing Research  
Given the current global need to reduce energy consumption and save energy in all areas, it is not surprising that efforts are devoted to designing and renovating buildings in order to achieve Zero Energy Buildings (ZEB) [1]. These buildings have been considered one of the promising strategies for decarbonisation, at least during building operation, due to its potential to reduce energy use and increase the total share of renewable energy through an efficient and integrated architectural design and energy system [2]. As the name suggests, ZEBs generate as much energy as they consume, usually from renewable energy resources, and align with global climate targets, significantly reducing greenhouse gas emissions in the building sector. In fact, the line followed for ZEBs has its origin in Directive 2010/31/EU [3] and is currently the most important normative reference at the European level in the field of building efficiency. These buildings are defined as buildings whose near-zero or very low energy requirements should be largely covered by energy from renewable sources, including energy produced on-site or nearby. However, despite the great virtues and advantages, the transition to ZEBs requires more supportive policies, regulations, and financial incentives to encourage their widespread adoption.  
Designing a ZEB means holistically integrating architectural solutions, technology, analytics, and automatic management systems throughout. Passive design harmonises the building with its surroundings and optimises its envelope by integrating advanced techniques and materials, so energy demands are minimised and local resources are considered. Generation technologies, both integrated and adjacent to the building, use renewable resources reducing dependence on fossil fuels. Knowing the usage patterns and profiles of building users enables us to define practices to promote energy efficiency among occupants. Finally, smart management systems allow us to monitor and control the energy use, including smart meters and Internet of Things (IoT). Interdisciplinary research is therefore needed in the field of energy-efficient buildings, combining knowledge from architecture, engineering, environmental sciences, and social sciences.
# PV Fault Detection Using Positive Unlabeled Learning
## 1 Introduction
We additionally demonstrate that using the same number of labeled samples, the PU learning algorithms both outperform a smaller supervised learning algorithm that does not take advantage of the unlabeled samples. This, in addition to the fact that it is the nature of the problem that it is easier to label a faulty sample than to guarantee that a sample is not faulty, confirms that given a labeling budget, it is more effective to label only faulty samples than to attempt to label both faulty and non-faulty ones. The main contributions of this paper are: (1) the use of a unique PU learning algorithm for solar array fault detection which has, to our knowledge, never been done before, (2) the adaptation of the MLR to work for solar fault detection, (3) the ability to effectively use significantly fewer labeled training data than most supervised learning algorithms by applying PU learning techniques to solar fault detection problems, (4) the introduction of a new PU learning algorithm, MLRf, designed to better detect and classify solar fault data, (5) the development of new comparative results demonstrating the effectiveness and robustness of the MLR and MLRf algorithms at detecting solar faults with very little labeled data, and (6) the demonstration that labeling \(x\) positive samples is more effective than labeling \(x\) total positive and negative samples. The novelty of this work lies most especially in the application of PU learning algorithms to solar fault detection and to the introduction of the MLRf algorithm.
# Dynamic Routings in Satellite Networks: An Overview
## 4 Existing Works on Dynamic Satellite Routings
### Dynamic Routings in the Multi-Layer Satellite Networks
#### 4.2.1 SDN-Based Dynamic Routing
To better verify the feasibility of various routing algorithms and the reconstruction of inter-satellite neighboring links, an experimental platform for SDN-based multi-layer satellite networks was designed. An ONOS-based SDN controller is used in the testbed to control multi-layer satellite networks. In addition, the demo platform is set up with 200 virtual satellite nodes to verify routing reconstruction. The integrated satellite-terrestrial network (ISN) is an emerging network framework to complement terrestrial 6G networks. In 2022, the work in [71] combined SDNs, artificial intelligence techniques, and fuzzy logic to design a new fuzzy CNN-based multi-task routing (FCMR) algorithm. The solution utilizes a two-stage LEO-GEO satellite network for long-distance, high-throughput, and low-latency data transmission. Based on the dynamic characteristics of the satellite, the scheme employs the GEO satellite and the ground computing center (GCC) as a common control plane. The GEO satellite mainly completes the collection of load data for each period, forming a multidimensional matrix. The GCC captures historical traffic data from the GEO controller for training and updating the convolutional neural network (CNN) model. The GEO satellite applies the trained CNN model for path planning and transmits the stream data stream to the LEO satellite. In addition, considering that CNN judgments may contradict the quality of experience (QoE), FCMR uses fuzzy reasoning to evaluate the task requirements, improve the output efficiency of CNN, and achieve the best ISN path assignment. To ensure QoE and load balancing, FCMR applies optimal ISLs to schedule multiple tasks. Simulation results show that FCMR has better network throughput, path-finding efficiency, and ISN congestion control. Compared with the basic CNN and Dijkstra routing algorithms, the proposedFCMR can employ fuzzy logic to enhance the decision flexibility of CNN. However, this subsection describes the latest dynamic routing algorithms for SDN-based multi-layer satellite networks. Moreover, the routing optimization algorithms in this subsection apply only to one satellite network model and no other constellation model in the longer term. Therefore, in future research, we need to further investigate and explore the suitable routing algorithms in all constellation models.
# A Stigmergy-Based Differential Evolution
## 6 Conclusions  
In this paper, we presented a modification to Differential Evolution that uses the global best to construct new candidate solutions. The adjustment considers stigmergy, a term taken from entomology to explain the indirect communication used by social insects to achieve complex projects. In the proposal, we eliminate the use of global best and instead we employ a local best. Like the real ants or termites, we utilize two neighbors of that local leader in the approach. The new candidate solution uses the leader's information and two neighbors in conjunction with a crossover operation as in the original DE. The final step in the approach is trace updating, which could modify any of the three individuals and their fitness values, simulating distributed and indirect communication with all the individuals in the artificial colony. We called this changed metaheuristic Stigmergic Differential Evolution (SDE). Due to the elimination of the global best, SDE has features that make it more parallelizable than DE, therefore, both algorithms were programmed in the CUDA-C language.  
We compared DE and SDE with 27 objective functions under the same circumstances. The results suggest that SDE achieves faster convergence than DE when the dimensionality increases and there are a larger number of individuals in the population, and it performs similarly to DE for 2D test functions, but DE converges in fewer iterations. It was demonstrated in [66] that canonical DE is adversely altered by increments in dimensionality, which affects in a super-linear fashion the convergence to the global optimum of the metaheuristic. In that sense, even though SDE has average convergence properties (e.g., Tables 5 and 6), we claim that its main feature is the capability to maintain a good convergence behavior as dimensionality grows.  
Figure 4: Percentage difference in execution times of DE and SDE for test functions with \(D=500\) and \(N_{p}=512\).  
The proposed modification could apply to other metaheuristics that use a global best to construct a candidate solution. A possible future work would consider the stigmergy-based adaptation of the original operators in other metaheuristic algorithms and their comparison.  
Conceptualization, V.O.-E.; methodology, V.O.-E.; software, V.O.-E. and E.G.-M.; validation, V.O.-E. and E.G.-M.; formal analysis, V.O.-E.; investigation, V.O.-E. and E.G.-M.; resources, V.O.-E. and [PERSON]-[PERSON]; writing--original draft preparation, V. O.-E.; writing--review and editing, E.G.-M.; visualization, V.O.-E. and [PERSON]-M.; supervision, V.O.-E. and [PERSON]-M.; project administration, V.O.-E. and [PERSON]-M.; funding acquisition, V.O.-E. and [PERSON]-M. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors wish to thank the Information and Communications Technology Laboratory of the Universidad Anahuac Mexico for allowing the use of computer equipment.  
The authors declare no conflict of interest.