esa-sceva/satcom-chunk-collection huggingface dataset:
# An Efficient Algorithm for Mapping Deep Learning Applications on the NoC Architecture
## 2 Related Work
### Application Mapping on the Network-on-Chip (NoC)
[6] presents an application mapping approach for each individual use-case by exploiting the dynamic voltage and frequency scaling (DVFS). This results in power saving; however, other performance factors needs to be explored as well. This work can be extended for the AI applications using the DVFS technique for obtaining power optimization. Ref. [35] describes a comparison of NoC application mapping for optimizing communication, energy consumption, and delay for VOPD and MPEG4 applications. The main focus is about exploring the application mapping for new design technologies, but it does not focus on the AI-based applications. An interesting approach is discussed in [36], where the authors discuss a mapping algorithm for decreasing latency and energy consumption in the hardware architecture. The application considered is spiking neuron networks (SNN), which have to be mapped on many-core neuromorphic hardware. However, this technique needs to be improved and it is an example of direct application mapping. For the sake of comparison, we have considered this type of direct technique in this article. Another recent article, [PERSON] [37] focuses on exploring the design space for the deep neural networks (DNNs) by focusing on the memory hierarchy design space. They claim to have a 64% better performance than state-of-the-art solutions, but this work can be further improved by focusing on the communication aspect. [PERSON]. [38] proposes a two step technique that is based on the genetic algorithm for mapping applications onto the NoC architecture. The proposed approach has a better result than the traditional genetic algorithm in terms of delay and power consumption; however, it does not consider AI-based applications. A comparison of the articles mentioned in the literature review is given in Table 2. Out of all these techniques, we found only a few articles that talk about mapping deep neural networks on the processing cores that are connected by the NoC. Therefore, our main focus in this article is to present a mapping strategy for the deep neural networks and explore this research domain.
## 4 Experimental Simulations  
### Evaluation Indexes  
The DTSP algorithm represents a solver for real-time approximate optimum path, which changes over time. To assess whether or not the dynamic Inver-Over evolutionary algorithm is effective, this paper reckons the errors between solutions of the dynamic algorithm and the static TSP algorithm as evaluation indexes.  
Figure 6: Execution process of Inver-Over operator: (**a**) Mutation; (**b**) Crossover.  
The formulas of absolute error \(\Delta e\) and relative error \(\overline{e}\) are expressed as follows:  
\[\Delta e(t)=L(t)-L^{*}(t) \tag{11}\]  
\[\overline{e}(t)=\frac{L(t)-L^{*}(t)}{L^{*}(t)} \tag{12}\]  
where, \(L^{*}(t)\) represents the optimal path length determined by the static TSP algorithm which is Inver-Over evolutionary algorithm. In this paper, the results obtained from the static TSP algorithm are hypothesized to be optimum. \(L(t)\) indicates the optimum solution at \(t\) determined by the DTSP algorithm which is dynamic Inver-Over evolutionary algorithm proposed in this paper.  
In the experimental process, all tests are performed for a space of 200 s and the optimal path is output every 1 s. Thus, the following results are obtained:  
Maximum error:  
\[\Delta e_{max}=\max_{t=1,\ldots,m}\{\Delta e(t)\}\qquad\overline{e}_{max}=\max _{t=1,\ldots,m}\{\overline{e}(t)\} \tag{13}\]  
Minimum error:  
\[\Delta e_{min}=\min_{t=1,\ldots,m}\{\Delta e(t)\}\qquad\overline{e}_{min}=\min _{t=1,\ldots,m}\{\overline{e}(t)\} \tag{14}\]  
Mean error:  
\[\Delta e_{a}=\frac{1}{m}\sum_{t=1}^{m}\left(\Delta e(t)\right)\qquad\overline{ e}_{a}=\frac{1}{m}\sum_{t=1}^{m}\left(\overline{e}(t)\right) \tag{15}\]
# Analysis and Applications of GlobeLand30: A Review
## 1 Introduction
Section 2 provides a brief introduction of GlobeLand30 data product and its accuracy analysis. The user distribution and application fields of GlobeLand30 are analyzed in Section 3. Section 4 presents three different types of applications, including status and change analysis, cause and consequence analysis, and coupling analysis with models. Section 5 discusses the future development and application of GlobeLand30.
## 3 System Validation
### Experimental Setup  
The VO algorithm proposed in this work was validated through a series of tests carried out in a controlled environment, as shown in Figure 5. For this purpose, a Cartesian gantry was built to allow the free movement of the camera in the horizontal plane. The sensor mounted on the setup was a 2-megapixel ELP USB camera (Ailipu Technology, Shenzhen, China) connected via USB to a basic laptop that was running the developed program in Python. During this initial testing, the program ran at 16 fps on a low-performance computer. This frame rate would only be suitable for slow-moving robots. For the intended application, this might be sufficient, but further optimizations on the software and hardware would be needed to apply this system more widely and maintain a good level of precision.  
Under the camera, different patterns can be placed to show the performance of the method in various environments. Specifically, the results were obtained using two different patterns, as shown in Figure 6: an Aruco pattern and a generic floor-tile texture. The Aruco in Figure 6 simulates a specialized surface with high texture where the features are easier to extract. It also demonstrates one key feature of this system. Placing scannable codes in the floor at specific points allows the robot to read out information in the environment. This can be used in several ways, like pointing out landmarks and keep-out areas or using it to provide absolute positioning. The pattern in Figure 6 mimics the random pattern of the ground tiles, validating the performance of the algorithm for its use in indoor environments. This pattern was generated using an initial design obtained with the cv::randpattern::RandomPatternGenerator class from OpenCV. Additional processing was applied to smooth the original pattern and make it resemble a real tile.  
Figure 5: Setup for the experiments.
# A Review and Experimental Evaluation on Split Learning
## 3 Experimental Evaluation
### Experimental Results and Analyses
#### 3.2.1 Comparison in the IID Setting  
First, we investigated the performance of different SL paradigms in the IID setting. The results are shown in Table 5. We compare the following five paradigms: Vanilla, U-shape, PSL, SGLR, and SplitFed. For most of the datasets we study in this paper, all SL paradigms achieved a similar accuracy and are comparable to the centralized one on IID data. For the ogbg-molhiv dataset, the model used is not suitable for U-shaped and SGLR. Given the significant imbalance in the labels of the dataset, we performed experiments specifically with \(\alpha=0.1\), not with \(\alpha=0\). For example, on the CIFAR-10 dataset with three clients, the vanilla SL paradigm achieved an accuracy of 90.08% while centralized training achieved 91.14%. In contrast, a commonly used U-shaped paradigm reached an accuracy of centralized training. For the graph dataset, the SL paradigms also achieved an AUC of 0.74, compared to an AUC of 0.7472 for centralized training.
# Emotion Recognition from Speech in a Subject-Independent Approach
## 5 Methods
### Preprocessing of Signals  
During preprocessing, silent segments preceding and following speech were removed, and no further operations were applied. The speech signal was then divided into one-second windows with shifts in either 0.1 or 0.5 s. Selecting a one-second window strikes  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Database** & **1 s** & **The Entire Statement** \\ \hline EMO-BAJKA & 0.40 +/\(-\) 0.18 & 0.58 +/\(-\) 0.14 \\ \hline RAVDESS & 0.28 +/\(-\) 0.14 & 0.61 +/\(-\) 0.22 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Results of emotion recognition in voice recordings by humans.  
a balance between capturing local manifestations of emotional expression and providing an acoustically rich, statistically stable sample in a subject-independent setup. Elementary speech features--such as fundamental frequency, energy modulations, articulation rate, and cepstral fluctuations--evolve over a few hundred milliseconds, but their reliable estimation requires observation spanning at least two to three full prosodic cycles [50]. A one-second window typically contains three to four syllables, encompassing a full intonational arc and brief micronarrative pauses [51]. Shorter segments (250-500 ms) in our tests increased feature variance and degraded classification performance, since isolated plosive consonants or recording artifacts exerted a disproportionate influence on the feature vector. Conversely, windows longer than one second proved impractical: although the RAVDESS includes two-second phrases, EMO-BAJKA contains them only sporadically, drastically reducing the number of training samples. Moreover, longer segments blend speech with silence and dynamic shifts, blurring the emotional signal and amplifying lexical dependencies, which in a subject-independent context leads to overfitting to specific speakers [52]. Thus, one-second windows with a 100 ms shift satisfy three key criteria: they provide sufficient examples for training and validation; they isolate purely acoustic carriers of emotion without semantic context; and they ensure stable estimation of prosodic and spectral parameters while preserving their local character [53].
## 6 Discussion  
As shown in Table 1, when the vibration amplitude was 3 mm, the wMPS could recognize the frequency of 9 Hz, and the absolute deviation was 1.06 \(\pm\) 1.35 mm. When the vibration amplitude was 30 mm, it could recognize the frequency of 4 Hz, and the absolute deviation was 3.28 \(\pm\) 4.40 mm. The GPS and RTS could recognize only the minimum amplitude of 5 mm, and only the frequency values of 4 and 3 Hz. When the vibration amplitude was 30 mm, only a frequency of 2 Hz could be recognized.  
Comparing these two results, we inferred some observations. Firstly, the wMPS in cooperation with an accelerometer can measure the intrinsic parameters while monitoring a small rigid structure, and it is more accurate than the GPS and RTS. It is simple to identify a vibration amplitude of 3 mm. Secondly, for a general large structure, the natural frequency is usually within 1 Hz, and the GPS dynamic-measurement accuracy fully meets the requirements. Also, the GPS range was not limited. Because the wMPS range is limited by its working principle, and the transmitting station cannot be installed in some places, the measurement range is limited. Therefore, a GPS is more convenient than the wMPS in large-structure monitoring.  
Figure 14: Distribution of discrete points. (**a**) Data at discrete points at 0.5, 1, 2, and 4 Hz; (**b**) projections of discrete points on the \(XY\), \(XZ\), and \(YZ\) planes.  
Figure 13: Variation curves of movement speed of sliding blocks.  
Thus, GPS, RTS, and the wMPS have their own advantages. The wMPS can directly monitor a rigid structure with an accelerometer. It can also be combined with GPS and RTS measurements to verify the accuracy of GPS and RTS monitoring of rigid structural data and analysis results. In the field of monitoring, with the application of the wMPS, a minimum value of the monitoring displacement amplitude can achieve a new level.
## 3 Results and Discussion
### Operational Carbon  
Passive strategies can have a significant effect on the CO2 emission, as shown in Figure 9 and Table 6. It is very interesting to notice that the CO2 concentration decreases between 5% and 10% with the application of passive strategies methods, and it is expected to increase between 10% and 15% from 2017 to 2050.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & & \multicolumn{2}{c}{**No Strategy**} & \multicolumn{2}{c}{**With Passive Strategies**} & \multicolumn{2}{c}{**2050**} \\ \hline
**Cities** & **Parameters** & **Min.** & **Max.** & **Min.** & **Max.** & **Min.** & **Max.** \\ \hline \multirow{2}{*}{Antsiranana} & Carbon & \multirow{2}{*}{50.6} & \multirow{2}{*}{71.0} & \multirow{2}{*}{47.8} & \multirow{2}{*}{67.0} & \multirow{2}{*}{59.0} & \multirow{2}{*}{79.0} \\  & (kgCO2/m2) & & & & & & \\ \hline \multirow{2}{*}{Mahajunga} & Carbon & \multirow{2}{*}{56.2} & \multirow{2}{*}{76.2} & \multirow{2}{*}{51.5} & \multirow{2}{*}{71.6} & \multirow{2}{*}{63.5} & \multirow{2}{*}{83.5} \\  & (kgCO2/m2) & & & & & & \\ \hline \multirow{2}{*}{Taolagnaro} & Carbon & \multirow{2}{*}{38.7} & \multirow{2}{*}{58.7} & \multirow{2}{*}{34.8} & \multirow{2}{*}{54.9} & \multirow{2}{*}{43.4} & \multirow{2}{*}{63.4} \\  & (kgCO2/m2) & & & & & & \\ \hline \multirow{2}{*}{Toamasina} & Carbon & \multirow{2}{*}{38.8} & \multirow{2}{*}{58.9} & \multirow{2}{*}{35.8} & \multirow{2}{*}{56.0} & \multirow{2}{*}{44.6} & \multirow{2}{*}{64.6} \\  & (kgCO2/m2) & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 6: Operational carbon in the four cities.  
Figure 8: Energy reduction in the four cities with application of passive strategies during two seasons.  
Mahajanga city showed the highest concentration of CO\({}_{2}\) while Taolagnaro city showed the lowest. It was noticed in several studies that the concentration of CO\({}_{2}\) converges in proportion to the energy demand. This effect is not random; in fact, the sources of electricity production generate CO\({}_{2}\) when they are not renewable.
## 4 Analysis of the Existing Datasets
### UCSD Dataset  
The videos in the UCSD dataset [82] contain events recorded in various crowd scenes. This was first published in 2010. The dataset includes anomalous actions, such as walking on grass, vehicles moving on the sidewalk and street, and unexpected behaviors such as skateboarding. Examples of abnormal frames are shown in Figure 4. In the Pedestrian (Ped) 1 sub-dataset, there were 34 training video samples and 36 test video samples, whereas  
Figure 3: Examples of frames from the UCF-Crime dataset are presented. Normal frames are shown in the first row, and abnormal frames are in the second row.  
\begin{table}
\begin{tabular}{l r r} \hline \hline
**Category** & **UCF-Crime** & **HR-Crime** \\ \hline Abuse & 50 & 38 \\ Arrest & 50 & 42 \\ Arson & 50 & 48 \\ Assault & 50 & 47 \\ Burglary & 100 & 96 \\ Explosion & 50 & 26 \\ Fighting & 50 & 39 \\ Road accident & 150 & 68 \\ Robbery & 150 & 145 \\ Shooting & 50 & 46 \\ Shoplifting & 50 & 50 \\ Stealing & 100 & 98 \\ Vandalism & 50 & 46 \\ Normal & 950 & 782 \\ \hline
**Total** & 1900 & 1571 \\ \hline \hline \end{tabular}
\end{table}
Table 8: The number of videos in the UCF-Crime dataset.  
the Ped2 sub-dataset has 16 training video samples and 12 test video samples. The Ped2 sub-dataset is generally used in studies. Ped1 has a low resolution and frame distortion [83]. The ground truth is available at both the pixel and frame levels.
# Operator-Based Adaptive Tracking Capacity Control in Complex Manufacturing Processes
## 2 Mathematical Model  
In the process of adaptive capacity controller design for a job shop, a mathematical model is an important factor for the controller's design. In this section, a funnel-based development model is discussed, and it integrates the customized flexibility of RMTs within complex manufacturing processes [29; 30]. In this model, the performance indicators considered are the input rate, output rate, and WIP level; these are not of interest for any single events or orders in the time domain. A general job shop manufacturing system, which consists of \(n\) workstations, is shown in Figure 1[5]. The parameters and variables in the mathematical model of this system are shown in Table 1.  
Due to the multiple parallel production paths of multiple products, the WIP levels of all workstations are generally higher than those in flow shop processes. Therefore, all machine tools are assumed to be working at the maximum productivity; then, the orders of the output rates of each workstation can be assumed to be the sum of all machines' capacities. Taking DMTs' high productivity into account, the capacity of a workstation includes the customizable part of DMTs and the changeable part of RMTs. This is given by  
\[\bar{c_{i}}(t)=n_{i}^{D}\cdot v_{i}^{D}+n_{i}^{R}\cdot v_{i}^{R}. \tag{1}\]If the number of RMTs is considered as a single input, then the output rate of the \(i\)th workstation is approximately equal to the capacity, which could be adjusted by the assigned number of RMTs. Then, the output rate is represented by  
\[X_{i}^{O}(t)=n_{i}^{D}\cdot v_{i}^{D}+u_{i}(t)\cdot v_{i}^{R}. \tag{2}\]  
As one of the key performance indicators, WIP has a great influence on productivity, delivery date, throughput, and cost. Therefore, the purpose of a capacity adjustment is to guarantee that every workstation works on an expected WIP level. Then, the WIP of each workstation can be described as the output signal of the model given by  
\[y_{i}(t)=\ y_{i}(0)+\int_{0}^{t}X_{i}^{I}(\tau)-(n_{i}^{D}\cdot v_{i}^{D}+u_{i} (\tau)\cdot v_{i}^{R})d\tau. \tag{3}\]  
However, customers' demands are volatile, and delays, including transportation delays between workstations and reconfiguration delays of RMTs, always exist; cf. Figure 1. Considering these factors, the mathematical model can be represented by  
\[ \begin{split} y_{i}(t)=&\ y_{i}(0)+\int_{0}^{t}X_{0 i}^{I}(\tau-\tau_{1})+X_{i}^{D}(\tau)\\ &+\sum_{j=1}^{n}p_{ji}\cdot(n_{j}^{D}\cdot v_{j}^{D}+u_{j}(\tau- \tau_{2}-\tau_{1})\cdot v_{i}^{R})\\ &-(n_{i}^{D}\cdot v_{i}^{D}+u_{i}(\tau-\tau_{2})\cdot v_{i}^{R}) d\tau,\end{split}  \tag{4}\]  
where the transportation time \(\tau_{1}\) is constant in this paper, and the reconfiguration delay \(\tau_{2}\) is \(\delta\) when RMT changes its operation from one to another workstation, but it only exists in the object, not in the original one. Thus, the reconfiguration delay is asynchronous and can be represented by  
\[\tau_{2}= \begin{cases}\delta,&u_{i}(t^{+})\geq u_{i}(t^{-}),\\ 0,&\text{else}.\end{cases}  \tag{5}\]
## 2 Materials and Methods
### Analytical Methods
#### 2.2.3 Analysis of Volatile Compounds by HS-SPME-GCMS  
The extraction of volatile compounds was done for all the different samples, by headspace solid-phase microextraction and analyzed by GC-MS (HS-SPME-GCMS).  
This analysis was carried out using a three-phase fiber (divinylbenzene (DVB)/Carboxen (CAR)/polydimethylsiloxane (PDMS), 50/30 \(\upmu\)m, Supelco). Before use, the fiber was conditioned in accordance with the manufacturer's recommendatios. In order to optimize the extraction, a preliminary study was done to choose the best extraction parameters (choice of fiber, extraction parameters). The selected extraction conditions were the following:  
For each gentian powder: 1 g of powder in a 20 mL vial were incubated in a water bath at 40 \({}^{\circ}\)C for 15 min. Then the fiber was exposed to the sample headspace for 15 min and was desorbed for 10 min into GC-MS. The analyses were done in technical triplicate;For each gentian macerated wines and the control wine: 10 mL of wine in a 20 mL vial were incubated in a water bath at 40 \({}^{\circ}\)C for 15 min. Then the fiber was exposed to the sample headspace for 30 min and was desorbed for 10 min into GC-MS. The analyses were done in technical triplicate.  
Volatiles were analyzed with a mass spectrometer (Agilent 5975C-VLMSD, electronic impact at 70 eV) paired with an Agilent 7890A gas chromatograph fitted with a split/splitless injector (240 \({}^{\circ}\)C). The chromatograph was equipped with a capillary column DBWAX of 30 m \(\times\) 0.32 mm (J&W Scientific). The film thickness was 0.25 \(\upmu\)m. Helium was used as carrier gas at a rate of 1.5 mL-min\({}^{-1}\) (average velocity of 44 cm-s\({}^{-1}\)). The temperature of the oven was increased from 40 \({}^{\circ}\)C to 240 \({}^{\circ}\)C at 4 \({}^{\circ}\)C-min\({}^{-1}\) and maintained 5 min at 240 \({}^{\circ}\)C. The injection temperature was 240 \({}^{\circ}\)C and was done in spitless mode.  
The mass spectrometer was used in scan mode from _m/z_ 29 to 400. The corresponding volatile compounds were tentatively identified by matching their spectral fragmentation with those provided by the mass spectral library of the National Institute of Standards and Technology (NIST) and the Wiley Registry (WILEY).  
In addition, for each volatile compound obtained, linear retention index (LRI) was calculated using the retention times of a standard mixture of C7-C30 saturated alkanes (Sigma-Aldrich) and compared with the LRI values published in the literature for columns with the same polarity.  
In order to identify robust gentian marker, volatile compounds systematically present at least in two of the three technical replicates and in the three biologic replicates for at least one of the two geographic sites have been kept. If a compound was absent in one of the three biologic triplicates by geographic site, it was eliminated for the statistical treatment.
## 4 Simulation and Result Analysis  
The simulation analysis was carried out in MATLAB/Simulink. The passive suspension and traditional CDC LTR damping on/off control (abbreviated as CDC-LTR) were compared with the CDC optimal distribution damping force strategy (abbreviated as CDC-ODDF) to illustrate the optimization effect. The CDC-LTR to prevent rollover simply sets the absolute threshold value of LTR parameters. When the threshold is reached, the anti-roll control is adopted for CDC suspension. Given that this research mainly focuses on tripped rollover while driving a vehicle, ESC differential braking and other methods of stability control are removed to highlight the control effect of a single CDC on rollover.  
For tripped rollover, the lateral force is the main source of the roll force. The lateral force may be caused by the turning of the vehicle itself in the driving process, or it may be caused by the larger lateral wind during the driving process. Therefore, the following is divided into two conditions for stability testing simulation analysis: fishhook and crosswind.  
The real vehicle selected for the funding project is an A0 class high-centroid vehicle. Although it has a high centroid, its mass is not large. Moreover, it is more prone to sideslip rather than rollover when that vehicle has no ESC system while driving. Therefore, the selection of its parameters is the critical condition obtained by repeated simulation, which can show the effect of vehicle control under a limited state.  
Figure 6: Range of variation of continuous damping control (CDC) damping force.
# Exploring the Performance of Continuous-Time Dynamic Link Prediction Algorithms
## 4 Towards More Targeted Negative Sampling Strategies for Dynamic Link Prediction  
The Birth-Death diagrams introduced in Section 3 illustrate the partitioning of nodes and edges into distinct categories: historical, overlap, and inductive. The comparison between a test event and a negative event involving an edge or node from any of these categories presents varying levels of difficulty for the task of discriminating the true event from the negative one. In this section, we operationalize these insights by formally defining Dynamic Link Prediction (DLP) and its connection to negative sampling (NS), before uncovering a taxonomy of the NS strategies targeting different aspects of DLP performance.