esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Current Issues and Challenges of Realising the Tactile Internet
### Unlicensed Band and More  
A significant number of wireless technologies work in the unlicensed band, i.e., 2.4 GHz to 5 GHz. There is a crowded situation regarding wireless technology's reliability within these freely available unlicensed bands. Because of this reliability, an examination was initiated to check for the substitute free spectrum for TI applications. With the availability of enormous bandwidth, millimetre waves (mm-wave) are especially appealing for TI [79]. Although mm-wave technology comes with a set of limitations and challenges for shorter-range communications, these issues also need to be taken care of [80].  
Several finger augmentation devices are wired, and aim to recover lost human senses wherein the user has lost the ability to feel objects. Because of the wired system, it limits the free mobility of hand gestures and creates a feeling of being tied to some objects. These devices have an electronic surface and should be worn on the tip of the fingers. In [81; 82], the radio-frequency finger augmented device (wireless) is considered to be a promising enabler for TI. This wireless system comprising of a reader worn on the wrist and a sensing tag worn on the fingertip makes use of an ultra-high frequency band ranging from 860 MHz to 960 MHz. Additionally, there are chances of impedance mismatch between the transmitting antenna and the electronic circuit, thus affecting the communication link. Self-tuning microchips address this issue. However, the concern remains regarding the distance between a transmitting antenna and an electronic circuit. If the distance is large between them, the tag will be disconnected and disrupt the normal functioning of the radio-frequency finger augmented device.
# Fast Scalable Architecture of a Near-ML Detector for a MIMO-QSM Receiver
## 4 Proposed Hardware Architecture for the Digital QSM Detector  
A hardware architecture that implements the detection algorithm presented in Section 3 was proposed. In order to illustrate it, the top module is shown first, and then, the internals of each module are explored.  
The design of the architecture followed a top-down approach, and it was composed of the data-path that included four modules and a control unit. The detector is presented in Figure 3. The **detland** **det2** modules implemented the first and second parts of the detection process, described in Algorithms 1 and 2, respectively; the **sort** module performed the sort operation used in the three algorithms; the **fd** module compared the distance metrics needed to determine the received symbol (inside the _for_ cycle in Algorithm 3); and the **ctrl** module provided the timing signals and activation flags for the data-path.  
From this point onwards, a bold line represents a bit vector, whereas a thin line represents a single bit.
## 4 Applications for IoT Technology in Smart Buildings
### Indoor Air Quality Monitoring (IAQ)  
The recent trend in the world is to create a bright and clean environment for reducing carbon footprints by 10% to 30%. IoT offers the easiest, most economical way to increase the infrastructure of the building to enable IAQ tracking in real-time [95]. Particulate Matter (PM) and Volatile Organic Compounds (VOC) Certifications are examples of current IAQ initiatives. This system establishes a method that uses evidence-based guidelines to reduce pollutant and contaminant concentrations. Arduino Uno board with ESP8266 (Espressif Systems, Shanghai, China) wireless transmission to transmit data between the devices, and Excel 2011 was used as a terminal to handle the data. Fuzzy logic rules were used to assess data from the interior environment [96]. The load was managed to utilize this information to create air quality monitoring systems that were more convenient than the threshold control systems of the past. In recent days, Blynk API (Application Programming Interface) can be installed in a building for collecting all the real-time data from sensors and the feedback from the users. This Blynk API can save up to 0.9 kWhr in the total energy consumption [97].
# A Trusted Federated Incentive Mechanism Based on Blockchain for 6G Network Data Security
## 5 Conclusions and Future Work  
In this paper, a system called ISFL-Sys is proposed, which comprises a blockchain module and a federated learning module. Within the blockchain module, an incentive mechanism based on smart contracts is introduced to boost the participation of edge nodes in training. A trusted federation learning mechanism for data security is designed in the federation learning module, which contains a new lightweight and efficient federation learning aggregation rule. Towards the end of the paper, a simulation platform is built using PyTorch. The results demonstrate that, in comparison with the Vanilla FL framework and the FedAvg framework, the predictive accuracy of ETFL continues to remain above 97%. In the future, further optimization of node selection schemes to reduce the workload of iterations will be explored. Privacy protection for model parameter gradients will be considered and more secure training strategies may be developed, possibly through the exploration of techniques such as homomorphic encryption and data augmentation algorithms.  
Writing--original draft, Y.L.; Writing--review & editing, B.G.; Visualization, H.Z.; Supervision, C.G. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Key Research and Development Project of China under Grant 2019 YFB2102303, Major Science and Technology Special Project of Yunnan Province 202202 AD080013, the National Natural Science Foundation of China under Grant 61971014.  
Not applicable.  
The data presented in this study are available on reasonable request-from the corresponding author.  
The authors declare no conflict of interest.  
Figure 14: Training time to achieve 95% accuracy.
# Detection of Large-Scale Floods Using Google Earth Engine and Google Colab
## 2 Materials and Methods
### Processing Chain
#### 2.3.2 Choice of Reference Sentinel-1 Images  
Reference images used in the change detection process are usually chosen from dates when there were no floods. There are several options when choosing them. We can choose images acquired during the dry season when water levels are at their lowest. However, this can lead to mistaking a stream bed filling up for a flood. We can also choose an image acquired just before the flood event, but in this case, the results would only factor in submersions caused by this specific event and could therefore include seasonal rains, which are not strictly speaking floods, or exclude subsequent submersions due to the floods. The best option would be to use radar images acquired when the main stream beds of water courses are full but not overflowing. However, this precise moment may not coincide with a satellite pass.  
An alternative approach is employed here for selecting reference images. For a given scene (post-flood image), all available images acquired during the same period in previous years are selected, starting from 2014. To guarantee at least one image for each year, for Sentinel-1, the period is extended to cover the 12 days preceding the event. A flood-detection operation is then applied to the post-flood image and each of these reference images. The results are combined using a majority-voting process to select pixels corresponding to water. This approach excludes zones that are usually under water (permanent water bodies) during the event in question.
# Iterative Interferometric Denoising Filter for Traveltime Picking
## 5 Computation Cost  
Our advocated method used symmetric patterns in both the spatial and temporal domains. This type of pattern can be used as a reliable indicator for early-arrival waveforms convolved with source wavelets. To evaluate the computational cost of the proposed method (Equation (4)), we can assume the trace number in our denoted window and the number of time steps of the temporal interferometric window as \(\mathbf{N_{t}}\). For each interferometric window, the total number of grid points utilized in Equation (4) is as follows:  
\[\mathbf{N_{s}}\times\mathbf{N_{t}}\]  
The values of \(\mathbf{N_{s}}\) and \(\mathbf{N_{t}}\) are supposed to agree with the proposed criteria in the Methods section. As an example, we assume \(\mathbf{N_{s}}=10\) and \(\mathbf{N_{t}}=10\) in an interferometric calculation; the total number of grid points used for an interferometric window calculation will be about 100. If there are 10,000 points in the offset-time domain in which we want to impose our interferometric filter in each iteration, the number of extra calculation operations is about \(10^{6}\). This number is not a challenging task for any CPU.
# Landslide Susceptibility Modeling Using a Deep Random Neural Network
## 2 Methodology
### Convolutional and Max-Pooling Layers  
Like CNN, DRNN uses the convolution layer and max-pooling layer to learn local relationships between landslide feature vectors. By using the convolution operation, each element in the sequence is connected to its \(K\) neighborhoods. The size of the convolution filter is assumed to be \(K\times 1\times D\). In this case, the signal is processed by convolution operation and is then nonlinearly activated. The process of convolution is expressed as:  
\[C_{i}=\sum_{n}^{N}f(\omega_{i}*x_{n}+b_{i})\;i\in[1,D],\;n\in[1,N] \tag{2}\]  
where, \(C_{i}\) and \(x_{n}\) represent the output and input of the convolution layer, respectively. \(\omega_{i}\) and \(b_{i}\) are weights and biases, respectively. \(f(\cdot)\) is the activation function. In this study, the ReLU function is used for the nonlinear transformation of signals.  
\[ReLU(x)=\left\{\begin{aligned} & x\geq 0\\ & 0\;x<0\end{aligned}\right. \tag{3}\]  
The aim of the pooling operation is to reduce parameters and increase the receptive field of the model. Assuming the max-pooling filter has a size of \(p\times 1\), the max-pooling layer integrates the convoluted features into a new sequence feature with a size of \(\frac{N-K+1}{p}\times 1\).  
\[P_{i}=\max_{p}(C_{i}) \tag{4}\]  
where \(P_{i}\) is the output of the max-pooling layer, which contains the main important information of \(C_{i}\). \(\max_{p}(\cdot)\) is the max-pooling function with a pooling size of \(p\times 1\).  
Figure 1: The whole architecture of the proposed DRNN.
# Two-Step Multiuser Equalization for Hybrid mmWave Massive MIMO GFDM Systems
## 3 Transmitter Design
### Partial CSI-Based Precoder  
The main purpose of this approach is to maximize the required user's power and conduct the beam to the dominant direction of the channel. Therefore, based on the channel model described in Section 2, first we must build the following matrix:  
\[\mathbf{A}_{tx,u}=[\mathbf{a}_{tx,u}(\phi_{1}^{tx,u}),\dots,\mathbf{a}_{tx,u}( \phi_{N_{d}}^{tx,u})]\in\mathbb{C}^{N_{tx}\times N_{d}}, \tag{9}\]  
where \(\mathbf{a}_{tx,u}(\phi_{q}^{tx,u}),\ q=0,\dots,N_{cl}-1\) is given by (3) and \(\phi_{q}^{tx,u}\) is the average AoD of the \(q\)th cluster. Then, the eigenvalue decomposition of \(\mathbf{A}_{tx,u}\mathbf{A}_{tx,u}^{H}=\mathbf{\Lambda}_{tx,u}\mathbf{\Sigma}_ {tx,u}\mathbf{A}_{tx,u}^{H}\) is computed. Finally, the dominant channel direction is selected to use in the analog precoder, making  
\[\mathbf{f}_{a,u}(n)=\frac{1}{\sqrt{N_{tx}}}\exp[j\arg(\mathbf{\Lambda}_{tx,u} (n,1))],\ \ n=1,\dots,N_{tx}. \tag{10}\]
# Data Preprocessing Method and API for Mining Processes from Cloud-Based Application Event Logs
## 6 SaaS Application Case Study
### Case Study Planning  
The main columns in the dataset that are required before applying PM techniques are the case identifier, the timestamp, and the activity.  
1. **Case ID:**The Company ID attribute is chosen as the case ID, as it refers to cross-site identifier used to differentiate users. The Company ID does not change from the starting point of the process (user sign up for trial version) until the last activity within the time frame that is included in the dataset, even if the user uses the trial version of the application sporadically over several days.
2. **Timestamp:** Each event occurs at a particular moment. The timestamp used here is in this format: YYYY-MM-DD hh:mm:ss.
3. **Activity:** From the event log dataset, the event attribute was selected as the activity. The event attribute represents the clicks and actions done by users while using the online application. Table 3 displays some event names (among 93) from the dataset, with their descriptions.
4. **Other attributes:** In addition to the main three attributes that are needed for PM analysis, the dataset includes additional columns that represent other attributes and properties that exist when an activity/action occurs. Here, those attributes include data source, data source number, help guide name, template name, account type, number of users, device type, browser, city, weekday, number of dashboards, dashboard type, and others. These attributes are considered as other necessary properties that will be used in the analysis.  
5. **Process mined from the original event log:** The initial process map generated by using the Disco tool [25] for the original event log, shown in Figure 4, is much too complex and unreadable for any analysis purpose. With high numbers of variants with respect to the cases, such spaghetti process maps are generated but are not helpful during analysis. Preprocessing and restructuring of the dataset are required.
# Reliability of a New Portable Dynamometer for Assessing Hip and Lower Limb Strength
## 2 Materials and Methods
### Data Analysis  
Custom-developed software (ARS Dynamometry, S2P, Ljubljana, Slovenia; created in Labview 8.1., National Instruments, Austin, TX, USA) was used for recording and analyzing the signals, which were sampled at 1000 Hz and pre-processed with a 10 ms moving average filter. The signals were transferred to a computer using a two-channel amplifier (InsAmp, Istol, Logatec, Slovenia) and an analogue-to-digital card (NIUSB-6009, National Instruments, Austin, TX, USA). The peak force values were multiplied by the lever arms (m) of the participants and normalized by their body mass to obtain normalized peak torques (Nm/kg) for each repetition. Lever arms were measured prior each test to the nearest 0.5 cm using the measurement scale on both edges of the dynamometer (shown in Figure 1) from sensor braces to: (i) greater trochanter for HFLX, HEXT, HABD, HADD; (ii) femoral epicondyle for HIR, HER, KEXT, and NHE; (iii) lateral malleoli for PFLX. Mean and maximal values of three repetitions, and agonist:antagonist ratio (%) for each test were calculated and statistically analyzed. No gravity corrections were performed for any strength test.
# A Multi-Machine and Multi-Modal Drift Detection (M2D2) Framework for Semiconductor Manufacturing
## 2 M2D2 System Framework  
Modern industrial environments often generate diverse modes of information simultaneously, including continuous numeric sensor readings (e.g., temperature, pressure, current), textual or semantic data (e.g., maintenance logs, operator notes, scheduling instructions), and categorical attributes such as batch codes, machine IDs, or plant locations. When extending these infrastructures to multi-machine or multi-plant collaborations, the data characteristics become increasingly complex and distributed. To meet the high-dimensional, dynamic, and privacy-conscious requirements of such settings, we propose M2D, which provides an integrated framework designed to achieve four primary objectives:  
1. Multi-Modal Data Integration and Baseline Modeling: Uniformly manage numeric sensor readings, textual content, and categorical labels; then, learn the baseline distribution of \"normal\" operating states via self-supervised or unsupervised methods.
2. Short- and Long-Term Drift Detection: Capture abrupt or acute drifts in real time, while also identifying latent, incremental deviations through periodic, batch-based analyses.
3. Interpretability and Active Learning: Once a warning is issued, offer visualized explanations of relevant sensor dimensions or textual keywords, and leverage human or automated feedback to reduce false alarms while continuously refining the model.
4. Drift-Aware Federated Learning: Address large-scale or geographically distributed industrial environments that require integrated knowledge sharing without violating data privacy, and mitigate the impact of severe local drift on the global model using dynamic weighting and micro-round strategies.  
Building on these foundations, M2D2 functions as an end-to-end framework, as illustrated in Figure 1. It integrates data acquisition, preprocessing, detection, explanation, and feedback into a closed-loop system that dynamically adapts to evolving manufacturing processes.  
Figure 1: M2D2 system framework.
### Research Objective  
With the long-term objective to develop low-cost WFOV optical systems for RSO detection suitable for operating on RSOs as small as nanosatellites, we have been developing a dual-purpose camera system that can serve as both an attitude sensor and SSA payload. With the support from the Canadian Space Agency (CSA) through the FAST Project # 19 FAYORA12, we developed a dual-purpose star tracker and demonstrated the concept on the STRATO-SCIENCE 2022 mission (payload depicted in Figure 1). Leveraging the experience gained from the balloon campaign, we are continuing to develop image-processing algorithms, such as [9], and Field Programmable Gate Array (FPGA)-based camera electronics. Additionally, we are conducting a mission concept study for future Canadian SSA Missions, in partnership with Defence Research and Development Canada (DRDC), Magellan, C-Core, and the University of Manitoba. As outlined in [10; 11], the results from the 2022 flight were promising. A total of 95,046 images were obtained under varying light conditions and altitudes during the mission. The images are being annotated and used for the development of RSO algorithms using AI and traditional object detection methods. This novel dataset is available upon request for algorithm development and validation.  
In this paper, we describe the second-generation mission, Resident Space Object Near-space Astrometric Research II (RSONAR II). This mission aimed to collect RSO images for varying resolutions to enhance our SSA dataset and demonstrate improved software on the 2023 flight with the dual objectives of attitude determination and real-time RSO detection from the stratospheric balloon platform at over 35 km in altitude. We also conducted a comparison study between two imaging systems and assessed the capability of each for stellar observation.
## 4 Case Study A: LED Array MIMO Multiplexing
### Transmitter  
**Stage 1. Prepare data:** After obtaining the text file, all the Non-ASCII values are removed. Then, each of the characters are converted to 8-bit binary data which are appended with starting and ending marker bits.  
**Stage 2. Assign each LED data:** 48 bits from the binary data for each LED (4 transmitters, thus, in total 192 bits) were parsed and a 11-bit Packet Number is assigned to each chunk of 48 bits of data. The 8-bit frame check sequence is computed over the Packet Number and data and repeated until there is no more input data available.  
**Stage 3. Packet formation:** To generate the packet, 5-bit barker codes are appended at the beginning of each packet and then the 11-bit Packet and 6 bytes of data were added. Finally, the 8-bit frame check sequence is also added and stored packets with the associated LEDs.
## 3 Motivation  
This research is inspired by real life systems. We have taken the motivation from hospital system situation. In following paragraph, we present an illustration of this situation.  
The operating theater (OT) constitutes the largest hospital cost category by 33% among the hospital departments [67; 68]. Therefore, operating room scheduling has been identified as a major challenge by hospital decision-makers. In addition, according to [PERSON] et al., the long waiting lists are the most received heard complaints in the health services [69]. Thus, the minimization of the patient waiting time is so interesting research area. By acting on this issue, we can improve the patient satisfaction and reduce the hospital costs. In fact, two types of operations are distinguished. The first is the elective operations when all the information are already known in advance. The second is the emergency operations which can occur at any time due to the urgent patients' arrival. These ones are uncertain and they are considered as a disruption for the already established schedule. On the other hand, the problem of a single operating room is usually studied in the literature since it can be extended to more complicated machine environment [60]. For example, [PERSON] et al. studied a stochastic optimization model for daily of a single operating room scheduling, then the authors extended their study in two stage stochastic resource [70]. This justifies the fact that we have modeled the problem as a single machine rescheduling problem. In addition, the patients' categories can be divided into five major groups according to their surgery priorities [71]. Thus, it is assumed for each job a weight representing its emergency level. In this study, the objective is to model the described practical example as a problem of a single machine rescheduling. We proceed in two phases. In the predictive phase (elective), the aim is to minimize the patient weighted waiting times as an efficiency criterion. Then, in the reactive phase (emergency), the aim is to minimize the efficiency criterion combined with a stability criterion, to reduce the deviation from the initial schedule. To the best of our knowledge, in the literature, there is not any contribution dealing with this practical example and simultaneously taking into account the two previous criteria. This described system can also be encountered in industrial system, as presented in [36], considering the waiting time as the waiting of materials before the reheating step in the oven and the arrival of new orders as a disruption. The weights can also be added for representing the customer priorities.