esa-sceva/satcom-chunk-collection huggingface dataset:
# On the Safe Deployment of Matrix Multiplication in Massively Parallel Safety-Related Systems
## 3 Enhancing MMM Safety
### Memory Hierarchy  
Applying previous checksum techniques to the matrix-matrix multiplication involves as many accesses to memory as the variables we intend to protect. As some implementations protect all variables involved in the matrix-matrix multiplication, the matrix-matrix multiplication computation entails protecting three times the number of performed matrix multiply-accumulate operations. In GPU-based implementations, the memory access speed is essential for achieving a good performance, mainly for real-time applications that are subject to strict execution-time requirements. Therefore, the type of memory chosen in the hierarchical memory model can be crucial. In our implementation, we allocate the ES into the global memory device. During the matrix-matrix multiplication execution, these ESs are transferred to registers (the memory with the highest speed) and returned to global memory when the computation is finished. In Figure 3, we depict the employed memory hierarchy. The grayscale used in the GPU memories denotes the memory access time, with the darkest gray requiring a higher latency.  
A particular concern arises in the CRC implementation, which requires further accesses to the lookup table. As all threads share these values, we have chosen shared memory instead of global memory to reduce memory access time. However, the shared memory has a reduced memory size that limits the CRC lookup table dimension. The safety designer  
Figure 3: ES transference among GPU memory hierarchies (\(CRC\_nElem\): number of CRC lookup table memory addresses. \(n\_Elem\): number of ES memory addresses).  
has to consider such a limitation at the design phase according to the target GPU's memory. In this paper, we perform CRC execution byte-by-byte, which requires 28 shared memory addresses and, hence, four accesses to shared memory per protected 32-bit data word.  
In Figure 3, we schematize the transfer of the CRC lookup table and the ESs across the memory hierarchy of the chosen platform. This transfer starts from the host memory, where it is initialized, to the shared memory, where it is finally accessed by each thread. Since GPU shared memory cannot be statically initialized, we initially store the CRC lookup table in constant memory before it is transferred to the shared memory at runtime. This memory presents a lower latency than global memory, especially in memory accesses where several threads access the same memory addresses consecutively. We can exploit this since the matrix-matrix multiplication executes as many blocks as \(k\) tiles it is decomposed in, and all these blocks store the CRC table in their own shared memory. Although the first block accesses occur serialized, the remaining blocks access the constant cache memory (which has the required 1 KB to store the entire table) that is faster than the global memory.
## Materials and Methods
### Volatile Compounds Analysis  
Five grams of each _LT_ muscle sample were weighted and minced and then transported to a 10 mL headspace extraction bottle after thawing. The bottle was sealed after adding 2 g sodium chloride (analytically pure) and 1 \(\mu\)L 0.1632 \(\mu\)g/\(\mu\)L 2-methyl-3-heptanone and fully mixed, followed by a constant temperature water bath at 90 \({}^{\circ}\)C for 1 h. Volatile compounds were isolated using a solid-phase microextraction (SPME) needle consisting of fused silica fiber with 50/30 \(\mu\)m thickness and 2 cm length of a DVB/CAR/PDMS (divinylbenzene/carboxen/polydimethylsiloxane) layer (Supelco, Bellefonte, PA, USA). The pre-prepared SPME needle (heated in the GC injection port at 270 \({}^{\circ}\)C for 1 h before the extraction) was put above the sample headspace for 40 min at 37 \({}^{\circ}\)C by a water bath to achieve equilibrium. The equilibrium time was calculated according to [PERSON] and [PERSON] [27]. The SPME fiber was desorbed directly at 250 \({}^{\circ}\)C for 5 min (2 min in splitless mode followed by 3 min with a purge flow of 60 mL/min) in the injection port of the GC and then prepared for the GC-MS analysis. After desorption of each sample, the fiber was cleaned for 2 min at 270 \({}^{\circ}\)C before the next extraction.  
The volatile compounds were measured using a gas chromatograph-mass spectrograph (GC-MS, QP2010 Plus, Shimadzu, Japan). An HP-INNOWax capillary column (Agilent, Santa Clara, CA, USA) of 30 m \(\times\) 0.32 mm I.D. \(\times\) 0.25 \(\upmu\)m film thickness was used for the separation of analytes. The setup of gas chromatography was as follows: Carrier: helium (5 mL/min); Pressure: 117.6 kPa; Column flow rate: 1.00 mL/min; Flow speed: 30.00 mL/min; and Temperature of inlet and interface: 250 \({}^{\circ}\)C. The procedure of temperature increase was: initial temperature was first set as 50 \({}^{\circ}\)C and kept for 1 min, then increased at a rate of 3 \({}^{\circ}\)C/min to 210 \({}^{\circ}\)C, followed by a 10 \({}^{\circ}\)C/min increase to 250 \({}^{\circ}\)C, and held for 18 min. The transfer line to the mass selective detector was kept at 150 \({}^{\circ}\)C. The setup of mass spectrograph was: Temperature of ion source: 230 \({}^{\circ}\)C; Quadrupole temperature: 150 \({}^{\circ}\)C; Ionizing voltage: EI (70 eV, 200 \(\upmu\)A); and Scan range: 30 to 400 (m/z).  
Volatile compounds were identified by the comparison of their mass spectra with the NIST 08 L Mass Spectral Database and the retention indices with those of known compounds. The linear retention index of each peak was calculated using a C6-C24 saturated alkane standard (Supelco, Bellefonte, PA, USA) as the same GC temperature program. The approximate quantities of the volatile compounds were estimated by comparing their peak areas with the n-alkane internal standard obtained from the total ion chromatogram. The results were expressed as means \(\pm\) standard error of all the repetitions in each group.
# InfNRS-QC: Crowd-Sourced Creation of a Dataset and Machine Learning Model for fNIRS Quality Control
## 2 Materials and Methods  
### Dataset  
The complete fNIRS dataset generated for this study consists of 4385 portions of single-channel fNIRS signals with a duration of 10 s each. In order to obtain a better time and space localization of signals' quality, short portions of signals were selected. To avoid biases introduced by different recording devices, all the signals have been collected using a NIRSport device (NIRx Medical Technologies LLC). This equipment has a scan rate of 7.81 Hz and employs LED emission with source wavelengths of 760 nm and 850 nm.  
Signals included within this dataset were drawn from four different studies, and all belong to adult participants. The first study (Mother-Child Synchrony study) involves the simultaneous recording of fNIRS data from mothers and children engaging in a passive video viewing task [19]. Only data from mothers (N = 31, Mean Age = 34.9 \(\pm\) 4.16 years) are selected for the dataset used in this work (for details on the experimental procedure, see [19]).  
The second study (Father-Child synchrony study) consists of fNIRS recordings of Fathers and Children engaging in both a passive video viewing task and an active play task. Only data from fathers (N = 29, Mean Age = 38.1 \(\pm\) 3.67) have been selected for the current dataset [20].  
The third study (3-Love study) consists of the data of 69 participants (Mean Age = 21.21 \(\pm\) 1.66) [21]. Participants were asked to watch three video clips depicting a couple interacting while baking, eating, and exercising. Before presentation, participants were informed (experimental manipulation) about the status of the couple, being either romantic partners, friends, or siblings.  
Finally, the fourth study (Mother-Father synchrony study) consists of the recordings of both mothers and fathers while passively hearing audio stimuli of infants' and adults' vocalizations [22].  
A breakdown of the number of signals per study is reported in Table 1.  
From the totality of the signals, 510 segments from the Mother-Child synchrony study were randomly selected for the current labeling stage. All the selected segments were resampled at 10 Hz, but no additional preprocessing step was conducted on the raw signals.
# A Performance Benchmark for the PostgreSQL and MySQL Databases
###### Abstract  
This study highlights the necessity for efficient database management in continuous authentication systems, which rely on large-scale behavioral biometric data such as keystroke patterns. A benchmarking framework was developed to evaluate the PostgreSQL and MySQL databases, minimizing repetitive coding through configurable functions and variables. The methodology involved experiments assessing select and insert queries under primary and complex conditions, simulating real-world scenarios. Our quantified results show PostgreSQL's superior performance in select operations. In primary tests, PostgreSQL's execution time for 1 million records ranged from 0.6 ms to 0.8 ms, while MySQL's ranged from 9 ms to 12 ms, indicating that PostgreSQL is about 13 times faster. For select queries with a where clause, PostgreSQL required 0.09 ms to 0.13 ms compared to MySQL's 0.9 ms to 1 ms, making it roughly 9 times more efficient. Insert operations were similar, with PostgreSQL at 0.0007 ms to 0.0014 ms and MySQL at 0.0010 ms to 0.0030 ms. In complex experiments with simultaneous operations, PostgreSQL maintained stable performance (0.7 ms to 0.9 ms for select queries during inserts), while MySQL's performance degraded significantly (7 ms to 13 ms). These findings underscore PostgreSQL's suitability for environments requiring low data latency and robust concurrent processing capabilities, making it ideal for continuous authentication systems.  
benchmark; postgreSQL; mySQL; database; biometrics +
Footnote †: journal: Software Interface
## 3 Results and Discussion
### Analysis of the Communication under Evaporation Ducts
#### 3.2.2 Communication Effect  
Based on the analysis of propagation characteristics, the bit-error-rate (BER) was introduced to evaluate the communication effect in the evaporation duct. According to communication theory, the BER in BPSK modulation is a function of SNR [8]:  
\[\text{BER }=0.5\cdot\text{erfc }\left(\text{SNR}^{0.5}\right) \tag{8}\]  
High-quality transmission (defined as BER < \(10^{-5}\)) in BPSK modulation occurs when the SNR at the receiving terminal is greater than 9.6 dB [8]. In contrast, the communication system is unable to work normally (BER > \(10^{-1}\)) when the SNR is less than \(-0.8\) dB [8]. As shown in Figure 8, the proportion of high-quality transmission during 9-11 o'clock was 97.72%, and the percentage grew to 98.72% from 12 to 14 o'clock. Communication effects suffered a night-time off-peak, with the ratio reduced to 86.20% from 19 to 4 o'clock.  
Communication quality could be further enhanced by using the advantageous period of the evaporation duct. The communication effect in the highest serviceable period (from 12 to 13 o'clock) is shown in Figure 9, and the communication system can be used normally without interruption (BER > \(10^{-1}\)) during this period.  
Figure 8: The percentage of high-quality transmission in the Taiwan Strait.  
The results showed that high-quality transmission could be achieved 98.90% of the time, throughout the year, and that 89.89% of the SNR exceeded 90 dB. Therefore, the communication system across the Taiwan Strait could be feasibly improved by using evaporation ducts.
## 4 Experiments
### Visualization of the Learned Representations  
In addition to evaluating UCDA's performance using ROC curves, we conducted further analysis of the datasets. This involved generating t-distributed Stochastic Neighbor  
\begin{table}
\begin{tabular}{c l c c c c c c} \hline \hline \multirow{2}{*}{**Labeled Data**} & \multirow{2}{*}{**Methods**} & \multicolumn{2}{c}{**PCam**} & \multicolumn{2}{c}{**LC25000**} & \multicolumn{2}{c}{**NCT-CRC-HE-100K**} \\ \cline{3-8}  & & **Accuracy** & **F1-Score** & **Accuracy** & **F1-Score** & **Accuracy** & **F1-Score** \\ \hline \multirow{4}{*}{1\%} & Rotation [34] & 80.81 & 80.68 & 88.27 & 88.25 & 88.27 & 88.01 \\  & DeepCluster [35] & 84.52 & 84.36 & 87.26 & 87.32 & 91.94 & 91.85 \\  & DARC [36] & 85.50 & 85.52 & 90.76 & 90.81 & 92.62 & 92.63 \\  & UCDA (Ours) & **86.83** & **86.60** & **90.91** & **90.89** & **94.65** & **94.72** \\ \hline \multirow{4}{*}{10\%} & Rotation [34] & 83.95 & 83.64 & 93.65 & 93.45 & 94.59 & 94.46 \\  & DeepCluster [35] & 87.56 & 87.50 & 97.76 & 97.66 & 97.57 & 97.65 \\  & DARC [36] & 87.77 & 87.7 & **97.95** & **97.97** & 98.57 & 98.50 \\  & UCDA (Ours) & **88.65** & **88.68** & 96.59 & 96.52 & **99.62** & **99.52** \\ \hline \multirow{4}{*}{50\%} & Rotation [34] & 86.03 & 86.12 & 97.61 & 97.66 & 97.85 & 97.89 \\  & DeepCluster [35] & 89.19 & 89.22 & 99.59 & 99.54 & 98.47 & 98.45 \\ \cline{1-1}  & DARC [36] & 88.75 & 88.7 & **99.88** & **99.81** & 99.57 & 99.53 \\ \cline{1-1}  & UCDA (Ours) & **89.78** & **89.63** & 96.36 & 96.30 & **99.65** & **99.60** \\ \hline \hline \end{tabular} Note: The bold content is the highest result in comparison.  
\end{table}
Table 1: Comparing performance on PCam and LC25000 datasets with different labeled data ratios.  
Figure 3: The ROC curves of our UCDA model on PCam and LC25000 datasets, where only 1% of the data were labeled.  
Embedding (t-SNE) visualizations to explore the extracted features. t-SNE is a widely used data visualization technique for dimensionality reduction, facilitating the intuitive representation of high-dimensional data and revealing its distribution.  
As illustrated in Figure 4, the t-SNE visualizations on PCam and LC25000 exhibit distinct separation of features extracted by the UCDA network into distinctive classes. This observation provides evidence that UCDA effectively captures discriminative information from medical images, resulting in improved classification accuracy of the model. The visualizations serve as compelling evidence of UCDA's efficacy in enhancing the model's performance for medical image classification tasks. They underscore UCDA's ability to accurately discriminate between different classes with a high level of precision.
## 1 Introduction
Indeed, simple image processing techniques are making standard the use of digital cameras for the detection of phenological events [15, 34, 35, 36], particularly since these cameras can be networked. As such they represent an easily acquired resource for augmenting large scale phenology monitoring based on the principles of near-surface remote sensing with a spatial coverage afforded by communications technologies [28, 37]. Rather than deploying cameras specifically for the task of phenological monitoring it has been suggested by [PERSON] _et al._ that the suitability of freely available public cameras associated with monitoring natural areas, roadway conditions, and for human surveillance be examined for detecting and monitoring plant phenology [28]. Their proposition was that since these cameras are already _in situ_ right across the conterminous United States of America these could afford the measurement of phenology across continents. The full potential of this was however compromised by the widely varying image resolution among the 1,100 public cameras identified, the labour intensive job of manually searching for suitable cameras and georeferencing them and finally the loss of data as a result of the changing of the internet addresses of the cameras used, permanent disconnection of cameras and changes in view angle. In this paper it is postulated that one way to overcome this limitation is to use a high density camera network operating the same hardware, for example a traffic camera network [38]. Here the utility of using traffic webcams for measuring phenology is explored and a methodology proposed that could be useful in fully automatically extracting phenological information from a network of sensors already in place but deployed for the purpose of traffic monitoring. It is surmised that key to any method that has the task of handling such networked data is that the vegetation with a phenological signal is found automatically with no human intervention required. Furthermore, the method must be robust with respect to camera movement and so not subject to loss of data.
# A Novel Hybrid Approach to Deal with DVL Malfunctions for Underwater Integrated Navigation Systems
## 4 Performance Evaluation
### Simulations and Results
Compared with the RBF predictor, the latitude and longitude precisions of the hybrid predictor are increased by 39.7% and 50% respectively. The advantages of the proposed hybrid predictor are apparent. As shown in Figure 8, the up velocity error and the height error of the system almost have no significant change during the malfunction period, which benefits from the external height information offered by the PS. As we know, the performance of the predictor degrades with increasing length of malfunction. To simulate longer DVL malfunctions (more than 100 s), the outputs of the DVL along three directions are set to be zero during the period 900-1020 s when the underwater vehicle is in right turning motion. Figure 9 shows the navigation errors of the four solutions when there is a DVL malfunction for a length of 120 s. Table 4 shows the error statistics of different solutions for various DVL malfunctions. Figure 8: Navigation errors when there is a DVL malfunction for a length of 60 s: (**a**) Velocity errors; (**b**) Position errors. According to the partial enlargement drawings in Figure 9a, the system with the hybrid predictor performs better than others during 120 s DVL malfunction. The data and the partial enlargement drawing in Figure 9b shows that the latitude errors of the systems with four different solutions stabilize at around 77.5 m, 18.7 m, 11.03 m and 5.3 m respectively, and the longitude errors stabilize at around 42.1 m, 26.6 m, 18.84 m and 7.8 m respectively. By using the hybrid predictor, the latitude and longitude precisions are increased by 71.7% and 70.7% respectively in comparison with the PLRS predictor. Compared with the RBF predictor, the latitude and longitude precisions of the hybrid predictor are increased by 51.9% and 58.6% respectively. The advantages of using the proposed hybrid predictor are more obvious in the situation of 120 s DVL malfunction than that of 60 s. It is because that the prediction precision of the other two predictors will decrease when the length of malfunction increases, but the SVR that predicts the residual components of PLRS can suppress the estimation errors to a certain extent. Consequently, the system with PLRS predictor or RBF predictor has larger horizontal position errors than the system with PLRS-SVR hybrid predictor. For both situations the hybrid predictor behaves better than the other two predictors. Based on Table 4, we can further confirm that the navigation results of the system with the PLRS-SVR predictor are superior to the other three solutions in both situations. It is worth mentioning that the position errors of the system with the proposed predictor remain less than 10 m during 120 s DVL malfunction, which is an excellent performance. Thus, it can be concluded that the hybrid predictor can effectively extend the tolerance time of the system on DVL malfunctions, which enhances the reliability and correctness of the underwater integrated navigation system.
## 3 Results
### Session 1: User Group A in Residential Care
This results in an overall success rate of 77.03%. The number of total turns includes 24 items that were queried more than once in the same assessment due to faulty system routing. In three cases, an assessment had to be aborted and restarted. In each of these cases, the participants had directed questions at the research team. The system logs show that these off-topic remarks were picked up by the system, interpreted wrongfully, and caused faulty routing. When the evaluation is cleansed of these aborted assessments, a success rate of 80.01% is calculated from a total of 136 turns and 109 error-free turns. Furthermore, the logs show that participants using non-valid answer options is the most common source of error in session 1. This occurred a total of 15 times and is thus the cause for approximately 44% of the off-script events. We categorized the detected off-script events, and present them along with their absolute frequency, in Table 2.  
In terms of the completion rate, an assessment is considered complete, if the required data on all items were collected. Based on the system logs, we found that four of the eight valid assessments in session 1 were completed, resulting in a completion rate of 50.0%. The success and completion rates for all sessions are listed in Table 3. As five of the eight older participants in session 1 had previous experience with the robot Pepper, we also calculated the success and completion rates separately for the experienced (group A1) and the inexperienced (group A2) users, as listed in Table 4. Group A1 achieves 79 successful turns, in relation to a total of 95 turns, resulting in a success rate of 83.16%. Out of the five interactions conducted, three were completed in full, thus bringing the completion rate to 60.00%. Group A2, the users without any previous experience with the robot score a lower success rate of 66.04% from a total of 53 turns with 35 successful ones. One out of these three interactions was completed, resulting in a completion rate of 33.33%. Additionally, the caregiver--not included in this calculation as they are outside of the user \begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Error Categories** & **Session 1 w/** & **Session 1 w/out** & **Session 2** & **Session 3** \\  & **Aborted Tries** & **Aborted Tries** & & \\ \hline No. of questions repeated (for whatever reason) & 24 & 22 & 0 & 0 \\ Overall off-script-events & 34 & 27 & 23 & 19 \\ Not a valid answer option & 15 & 11 & 7 & 3 \\ Off-topic remark/remark directed at researcher & 8 & 6 & 0 & 2 \\ System error (correct input matched wrong) & 4 & 4 & 1 & 0 \\ User uttering spoken too softly/no speech recognized & 3 & 3 & 7 & 10 \\ STT error (system understood correct input wrong) & 3 & 2 & 2 & 4 \\ Training data error & 1 & 1 & 6 & 0 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The error sources of the robot-assisted health assessments are shown. group of older adults--achieved a success rate of 93.75% and were able to complete the health assessment.