esa-sceva/satcom-chunk-collection huggingface dataset:
# RMRRASL: Robust Matrix Factorization with Robust Adaptive Structure Learning for Feature Selection
## 5 Experiments and Analysis
### Analysis of Experimental Results
#### 5.5.3 Convergence Analysis  
In this subsection, we set the parameters to their respective optimal combinations in each of the five databases; then, iterative convergence analysis was performed. Figure 8 shows that RMFRASL achieved convergence with a small number of iterations on each database. Moreover, we provide the number of iterations needed by the compared methods to achieve convergence in Table 11. According to this table, the number of iterations of the proposed RMFRASL method was lower than that of the other methods in most cases.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **AR** & **CMU PIE** & **Extended YaleB** & **ORL** & **COIL20** \\ \hline LSFS & 63.00 \(\pm\) 0.75 & 52.81 \(\pm\) 0.51 & 55.13 \(\pm\) 1.40 & 75.96 \(\pm\) 1.18 & 64.92 \(\pm\) 1.71 \\ RNEFS & 62.87 \(\pm\) 0.77 & 53.64 \(\pm\) 0.55 & 55.51 \(\pm\) 0.82 & 78.13 \(\pm\) 0.82 & 65.55 \(\pm\) 1.42 \\ USFS & 63.04 \(\pm\) 1.14 & 54.01 \(\pm\) 1.10 & 56.10 \(\pm\) 0.91 & 76.71 \(\pm\) 1.61 & 67.92 \(\pm\) 1.69 \\ CNAFS & 63.03 \(\pm\) 1.01 & 54.63 \(\pm\) 0.82 & 56.34 \(\pm\) 1.37 & 75.90 \(\pm\) 0.00 & 68.66 \(\pm\) 2.15 \\ NNSAFS & 63.99 \(\pm\) 1.17 & 53.78 \(\pm\) 1.25 & 56.26 \(\pm\) 1.38 & 72.26 \(\pm\) 2.43 & 69.19 \(\pm\) 2.38 \\ RSR & 63.53 \(\pm\) 0.91 & 54.98 \(\pm\) 1.06 & 56.66 \(\pm\) 1.69 & 76.09 \(\pm\) 1.47 & 66.60 \(\pm\) 1.91 \\ SPNFSR & 64.68 \(\pm\) 0.66 & 55.18 \(\pm\) 0.86 & 57.18 \(\pm\) 1.05 & 78.68 \(\pm\) 1.41 & 70.89 \(\pm\) 1.53 \\ RMFRASL & 65.56 \(\pm\) 1.04 & 56.66 \(\pm\) 0.97 & 57.26 \(\pm\) 1.87 & 78.78 \(\pm\) 1.24 & 72.56 \(\pm\) 1.07 \\ \hline \hline \end{tabular}
\end{table}
Table 10: The best NMI% and STD of different algorithms on the five databases.  
Figure 8: _Cont._
# E-Waste Challenges in India: Environmental and Human Health Impacts
## 5 Negative Impact of E-Waste on the Environment
### Negative Impact on Air Quality:  
The atmosphere becomes contaminated by informal disposal methods such as shredding, dismantling, or melting electronic material, which release airborne dust particles and release toxic chemicals, including dioxins and fumes, leading to atmospheric pollution [60]. This affects respiratory function and can potentially harm the lungs [61; 62]. Metals discharged during manual processing include barium (Ba), arsenic (As), lithium (Li), lead (Pb), and cadmium (Cd) [61]. Burning electronic waste releases fine particles that impact animals, birds, humans, and other organisms, especially those living in the vicinity. Humans are at higher risk because these fine particles are difficult to manage and can cause cancer, tumors, and other chronic diseases [63]. Precious metals, including gold, copper, and silver, were typically recovered by advanced incorporated electronics with the use of acids (for example, hydrochloric, nitric acid), desoldering tools, and various chemical agents that discharge fumes and dust during informal recycling [60; 61]. These dust particles can travel thousands of miles, affecting those handling informal electronic waste recycling practices. Electronic incineration emits harmful chlorinated and brominated gases, including furan, dioxins, and greenhouse gases, including carbon dioxide (CO\({}_{2}\)) and nitrogen oxide (N\({}_{2}\)O), which are major contributors to greenhouse effects that lead to global warming and further accelerate the depletion of the ozone layer. Over time, air pollution also affects soil, water quality, and biodiversity, causing irreversible environmental damage [62]. Informal recycling practices can also release high concentrations of lead within the environment, which are absorbed through inhalation and ingestion. Lead causes damage to wildlife, plants, animals, and human inhabitants of the affected area [59; 60].
## 3 Results and Discussion
Since the performance of hydrographic surveys is related to dynamic measurements, it can be concluded that none of the receivers installed in smartphones complied with the requirements of this order. On the other hand, for the order 1a/1b, models S4 and S5 (with dual-system GPS/GLONASS modules) deviated only slightly from the minimum availability requirements (95%). In the analyzed dynamic tests, the availability factor for models S4 and S5 reached 88.28% and 88.68%, respectively. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \multirow{2}{*}{**Year.**} & \multirow{2}{*}{**System**} & \multicolumn{2}{c}{**Special Order**} & \multicolumn{2}{c}{**Order 1a/1b**} & \multicolumn{2}{c}{**Order 2**} \\ \cline{3-8}  & & \multicolumn{2}{c}{**Availability**} & \multicolumn{2}{c}{**Availability**} & \multicolumn{2}{c}{**Availability**} \\ \cline{3-8}  & & **Stationary** & **Dynamic** & **Stationary** & **Dynamic** & **Stationary** & **Dynamic** \\ \hline
**2014 (S)/2017 (D)** & DGPS & **99.76** & **98.09** & **100** & **100** & **100** & **100** \\ \hline
2014 (S)/2017 (D) & EGNOS & **89.11** & **97.52** & **100** & **100** & **100** & **100** \\ \hline
2017 & Samsung Galaxy Y & **54.88** & **30.73** & **99.39** & **66.02** & **100** & **100** \\ \hline
2017 & Samsung Galaxy S3 & \multirow{2}{*}{**14.39**} & \multirow{2}{*}{**25.52**} & \multirow{2}{*}{**99.16**} & \multirow{2}{*}{**97.85**} & \multirow{2}{*}{**100**} & \multirow{2}{*}{**100**} \\  & Mini & & & & & & \\ \hline
2017 & Samsung Galaxy S4 & **95.7** & **28.67** & **99.98** & **88.68** & **100** & **100** \\ \hline
2017 & Samsung Galaxy S5 & **96.36** & **27.92** & **100** & **88.28** & **100** & **100** \\ \hline
2017 & Samsung Galaxy S6 & **14.99** & **45.92** & **58.82** & **87.7** & **97.54** & **99.45** \\ \hline
2017 & Samsung Galaxy S7 & **25.35** & **20.84** & **75.38** & **74.21** & **99.76** & **98.67** \\ \hline \end{tabular}
\end{table} 
Table 4: A summary of the availability factor values, determined for the DGPS and EGNOS systems, as well as multi-GNSS receivers in the context of minimum positioning requirements determined for four IHO orders. The measurements relate to stationary and dynamic tests. It should be very clearly stressed that in order to present the proposed method, this article used measurement data from various tests on DGPS and EGNOS systems and multi-GNSS receivers. The method presents a new approach to the assessment of the systems' possibilities in terms of the IHO requirements, since for the assessment of the system's accuracy and availability, mathematical models based on life and failure times were used instead of the position errors typically adopted in navigation. The fact that the analyses were carried out on data originating from different years is of no significance here, as the purpose of this publication is to present the original author's method. The above-mentioned results of the measurement campaigns enabled the verification of the model based on actual data.
# Exploring the Data Efficiency of Cross-Lingual Post-Training in Pretrained Language Models
## 4 Experimental Setup
### Dataset  
We use Korean Wikipedia (Wiki-ko) for post-training in \(L_{2}\). The Wikipedia dump from September 2020 was downloaded and extracted using the WikiExtractor [55] tool. This raw text is split into sentences and tokenized using SentencePiece [56] with a vocabulary size of 50 K tokens. This resulted in 4.19 M sentences with 61 M words before tokenization. The dataset is split into 4 M/100 K/88 K sentences to be used as train/valid/test splits, respectively.  
For extrinsic evaluations, we use the following four tasks to quantitatively compare different models. Detailed statistics of each dataset is summarized in Table 1.  
**PAWS-X--We use the Korean portion of the PAWS-X [57] dataset. The goal is to identify whether the given sentence pairs are a paraphrasing of each other or not. We report the classification accuracy (%) for this dataset.**  
**KorSTS--The Semantic Textual Similarity (STS) [58] dataset aims at assessing the semantic similarity between a pair of sentences. Each pair is assigned with a score ranging from 0 to 5, and the model's performance is measured using Spearman's rank correlation coefficient against the gold labels. The KorSTS [59] dataset provides the machine-translated train examples as well as the human-translated development and test examples of the STS dataset.**  
**KQP--The Korean Question Pairs (KQP) [60] is a dataset analogous to the Quora Question Pairs (QQP) [61] dataset, in which the model needs to identify if the given two questions convey the same meaning. KQP consists of 7.6 K human-annotated question pairs. Accuracy (%) is used as the evaluation metric.**  
**KHS--The Korean Hate Speech (KHS) [62] dataset is a collection of 8367 news comments, labeled as one of \"hate\", \"offensive\", or \"none\" by human annotators. As gold labels for the test split are unavailable, we report the best f1-score on the validation split for this task.**  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline \multirow{2}{*}{**Task**} & \multicolumn{2}{c}{**Train**} & \multicolumn{2}{c}{**Validation**} & \multicolumn{2}{c}{**Test**} \\ \cline{2-7}  & **\# Examples** & **\# Tokens** & **\# Examples** & **\# Tokens** & **\# Examples** & **\# Tokens** \\ \hline PAWS-X & 49,401 & 1,413,443 & 2000 & 50,292 & 2000 & 50,599 \\ KorSTS & 5749 & 86,253 & 1499 & 26,022 & 1378 & 21,066 \\ KQP & 6136 & 46,007 & 682 & 5067 & 758 & 5589 \\ KHS & 7896 & 129,422 & 471 & 7755 & N/A & N/A \\ \hline \hline \end{tabular}
\end{table}
Table 1: Statistics of the datasets used in the downstream evaluations.
# Optimizing the Scheduling of Teaching Activities in a Faculty
## 1 Introduction
### Objectives  
The main objective is to create and execute a resilient system that automates the process of generating timetables for the faculty. This will be achieved through the development of an Automated Timetable Generation System. The system will consider multiple constraints, preferences, and optimization criteria to generate timetables of superior quality.  
The system should have the capability to optimize the use of time slots, faculty, and rooms, ensuring better resource management and minimizing potential conflicts. By automating course allocation, the project aims to reduce instances of scheduling clashes, such as faculty members being assigned to multiple classes simultaneously or students having overlapping courses.  
Additionally, the system will also enhance the satisfaction of both students and faculty by taking into account their preferences and availability, ultimately leading to a more efficient and well-coordinated academic environment.
## 3 Proposal of Ground Station Direction Detection Method Using Color Markers
### Color Marker Detection Algorithm
#### 3.1.4 Operation of the Proposed Method  
The operation results of the method described in Sections 3.1.1 and 3.1.2 up to the detection of RCMs from captured images are shown in Figure 5. The image processing required for RCM detection was implemented using OpenCV 4.9.0 [12]. Figure 5a is the image captured by the camera, and Figure 5b is the result of blob detection processing on the image in Figure 5a. The detected blob coordinates are indicated by black dots. The blue dots in Figure 5c show the predicted positions of the markers in the image. The result of RCM detection based on the information in Figure 5b,c is the green dot in Figure 5d. As shown in Figure 5d, it can be confirmed that RCMs are detected accurately even when there are objects of the same color around the color markers. In a single frame, the processing time for Figure 5a-d was 0.062 s. In other words, the proposed method can detect 16.13 frames of color markers per second.
# Dynamic Collaborative Optimization Method for Real-Time Multi-Object Tracking
###### Abstract  
Multi-object tracking still faces significant challenges in complex conditions such as dense scenes, occlusion environments, and non-linear motion, especially regarding the detection and identity maintenance of small objects. To tackle these issues, this paper proposes a multi-modal fusion tracking framework that realizes high-precision tracking in complex scenarios by collaboratively optimizing feature enhancement and motion prediction. Firstly, a multi-scale feature adaptive enhancement (MS-FAE) module is designed, integrating multi-level features and introducing a small object adaptive attention mechanism to enhance the representation ability for small objects. Secondly, a cross-frame feature association module (CFAM) is put forward, constructing a global semantic association network via grouped cross-attention and a memory recall mechanism to solve the matching difficulties in occlusion and dense scenes. Thirdly, a Dynamic Motion Model (DMM) is developed, enabling the robust prediction of non-linear motion based on an improved Kalman filter framework. Finally, a Bi-modal dynamic decision method (BDDM) is devised to fuse appearance and motion information for hierarchical decision making. Experiments conducted on multiple public datasets, including MOT17, MOT20, and VisDrone-MOT, demonstrate that this method remarkably improves tracking accuracy while maintaining real-time performance. On the MOT17 test set, it achieves 63.7% in HOTA, 61.4 FPS in processing speed, and 79.4% in IDF1, outperforming current state-of-the-art tracking algorithms.  
multi-object track; feature enhancement; cross-frame feature association; small object track +
Footnote †: journal: Applied sciences  
0000-0001-5000-0002-3899][PERSON]. [PERSON] et al.
## 2 Data
### Automatic Passenger Counting System Data  
Approximately every seventh commuter train vehicle in Stockholm is equipped with an automatic passenger counting (APC) system. Boarding and alighting passengers are counted at the doors of the carriages equipped with an APC system. To detect passengers moving through the door, the system makes use of photocells which are located at floor level on the train doors. To distinguish between boarding and alighting passengers, the system consists of two cells that enable the system to detect the direction of movement. There is no minimum height requirement of an object for it to be detected due to the placement of the photocells, all movement through the doors is thus collected. The counts from the  
Figure 1: Schematic layout of the commuter train network in Stockholm.  
APC system are then aggregated for the entire train and are corrected based on historical passenger counts when necessary. Using this historical data ensures the accuracy of the data even at high passenger volumes. The data we obtained from the APC system contain two pieces of relevant information: the timestamps and dwell times with a resolution of seconds, and the number of boarding and alighting passengers aggregated over a train. In total, we have about 1,370,000 unique station stops in Stockholm with information on passenger volumes aggregated on a train level and dwell times on a magnitude of seconds covering 2013-2016. Although the APC data are somewhat dated, both the planning principles and rolling stock characteristics during the period for which we have data are similar to the situation as it is currently.
## 3 Materials and Methods
### Assessment of Antimicrobial Activity  
The disc diffusion method was used to assess the effect of the analyzed hydrogels on the growth of microorganisms (pathogenic and probiotic bacteria or fungi). The analyzed strains were obtained from the American Type Culture Collection (Manassas, USA). The organisms that were used in the tests were bacteria _Staphylococcus aureus_ ATCC\({}^{\circled\) BAA2312(tm), _Staphylococcus epidermidis_ ATCC(c) 49134(tm), _Staphylococcus capitis_ ATCC(c) 146(tm), _Microococcus luteus_ ATCC(c) 10240(tm), _Propionibacterium acnes_ ATCC(c) 11827(tm), _Corynebacterium_ ATCC(c) 373(tm) ATCC(c) 11827(tm), _Bacillus subtilis_ ATCC(c) 11774(tm), and _Kocuria kristine_ ATCC(c) BAA-752(tm) and the probiotic strain Lactobacillus acidophilus_ ATCC(c) 314(tm). Additionally, determination of the zone of inhibition of growth was performed for _Candida albicans_ ATCC(c) 14053(tm) and _Malassezia furfur_ ATCC(c) 14521(tm) fungi. Initially, 10 mL samples of agar media appropriate for each bacterial and fungal strain were poured onto sterile Petri dishes. For this purpose, Tryptic Soy Agar, Mannitol Salt Agar, Nutrient Agar, Tryptone Soya Agar Blood Agar, Blood Agar, LB Agar, Brain Heart Infusion Agar, Yeast Malt Agar, Sabouraud Dextrose Agar, and MRS Agar were used. Then, the media were inoculated with individual strains of microorganisms with a density of 5.0 \(\times\) 10\({}^{7}\) CFU/mL. Then, extracts of the analyzed hydrogels were prepared at 1.0% and 5.0% concentrations and sterilized by membrane filters (0.22 um). Sterile discs of filter paper (6 mm in diameter) were soaked in various concentrations of hydrogels. A disc soaked in sterile distilled water was used as a negative control. The inoculated plates with the impregnated discs were left at 4.0 \({}^{\circ}\)C for 2 h to allow the compounds to pre-diffuse through the medium, and then the plates were transferred to an incubator. Cultures were carried out in aerobic or anaerobic conditions depending on the requirements of the microorganism. The effect of the test compounds on the growth of the tested microbial strains was assessed by measuring the diameter of the inhibition zone after 48 h of cultivation at 35.0 \(\pm\) 2 \({}^{\circ}\)C.
## 7 SensorNC Applications
### TDMA Optimization  
In this application (see also [8]), we present a means to find energy-efficient medium assignments in time-slotted WSNs that satisfy given real-time constraints. Specifically, we present a way to find the optimal length of time slots and cycles in TDMA schemes. This problem is solved analytically to find solutions for a general sink tree network under a fluid SensorNC model.  
When designing a TDMA system, a choice has to be made for how long the repetitive TDMA frame, as well as the individual slot sizes of each participating node are. For some network nodes, that just requires a short slot in which they can send collected data and perhaps receive an acknowledgment from a downstream node. However, in larger WSNs, some nodes have higher bandwidth requirements for forwarding other nodes' data, while collecting and sending data themselves. Aside from avoiding contention, using TDMA also reduces energy consumption by making it possible for nodes to power down in periods without relevant traffic.  
Since in WSNs, two main concerns are minimizing power consumption and meeting delay bounds, while transmission bandwidth requirements tend to be low, we want to maximize the frame length, giving the sensor nodes the opportunity to disable their radio transceivers or even go into deep sleep modes.
# Dynamic Information Flow Tracking: Taxonomy, Challenges, and Opportunities
## 6 Discussion and Prospect
### DIFT Works on Different Layers of Whole Systems  
As shown in Figure 1, we give an overview of DIFT techniques on different layers of whole systems. In the _program-layer_, the target program is modified further to support the DIFT-related operations through binary instrumentation and compiler aided methods. In the _OS layer_, the target program and DIFT operations are divided and placed into different threads (main thread and helper threads) on different processor cores. The synchronization between the main thread and helper threads is finished through shared memory and a special hardware channel, e.g., a FIFO message queue. The hardware-assisted methods provided in the _architecture layer_ help accelerate the DIFT-related operations. The corresponding hardware components and modules are extended to support the tag-related operations.  
Full-system-based design is a good candidate for DIFT frameworks because this type of design acquires the essential information from the three layers mentioned above and combines the benefits from both software and hardware DIFT designs. In addition to the tag-related logic in the _architecture layer_, this layer also needs to provide isolation an environment for secure execution. The storage of data and corresponding tag need to be protected properly. Using a hypervisor may be a good method to protect the tag storage and DIFT-related handler. Based on the architectural support, the OS enforces the security rules and provides a mechanism to ensure the security features provided from the _architecture layer_ can be used in a safe manner. For example, it is possible to implement an extra machine mode, e.g., monitor mode, orthogonal to the user mode and supervisor mode. Further, the DIFT-related operations can only be finished in this monitor mode. Finally, the program in the _program layer_ is used to run the instructions, execute the tag-related operations and invoke the extra machine mode provided by the OS.  
Figure 1: The DIFT works on different layers of whole systems.
## 3 Results and Discussion
### PPF Model's Results
#### 3.1.3 RNN Model  
Regarding the RNN model, the FFNN's set of parameters, as well as its delay parameter, must be fit. Therefore, the training algorithm, number of hidden layers and each layer's neurons activation functions remained the same as in the FFNN's model. As occurred for the FFNN forecaster, the literature does not provide an analytical method for choosing the hidden layer's number of neurons or the RNN's characteristic delay parameter, so both parameters had to be chosen through sensitivity analyses (see Figure 2).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Structure** & **Hidden Layer** & **Training** & **Validation** \\  & **Neurons** & **RMSE (MW)** & **RMSE (MW)** \\ \hline
1 & 5 & 0.0878 & 0.0790 \\
2 & 10 & 0.0845 & 0.0776 \\
3 & **15** & **0.0853** & **0.0766** \\
4 & 20 & 0.0861 & 0.0774 \\
5 & 25 & 0.0862 & 0.0773 \\ \hline \hline \end{tabular}
\end{table}
Table 2: FFNNs structures’ averaged results for fixing hidden layer’s number of neurons.  
Figure 1: FFNN forecaster’s layout.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **RMSE Train. (MW)** & **RMSE Val. (MW)** & **R2 Train. (-)** & **R2 Val. (-)** \\ \hline Value & 0.0853 & 0.0766 & 0.9955 & 0.09959 \\ Improve (\%) & 31.98\% & 39.15\% & 0.59\% & 0.71\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Computed RMSE and R\({}^{2}\) index values for 15-neuron FFNN model.  
Because we had to select two parameters, we first modified the delay while holding the number of neurons constant, and then having chosen the delay, we modified the number of neurons. In this study, the procedure explained in [45] was used, where the delay was varied from 1:2 to 1:6 while the number of neurons was held at 10. After the best delay was obtained, it was held constant while the number of neurons was modified from 5 to 15, in steps of 5 neurons. The same procedure applied in the FFNN sensitivity analyses for avoiding non-convergence risk was used for the RNN forecaster. Thus, each configuration's five tests were run and their training and validation results were averaged (see Table 4).  
The results in Table 4 indicate that the first structure analyzed, where the delay was 1:2 and 10 neurons were in the hidden layer, produced more accurate predictions for both databases. Therefore, this structure was chosen in order to compare it against the other PPFs results, and Table 5 summarizes this configuration's error metrics, as well as their improvement as a percentage against the benchmark model.