esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
The wavelength range around 2 \(\upmu\)m is important for environmental spectroscopy, laser processing, biomedical imaging and medicine, laser imaging detection and ranging (LIDAR), etc. Thulium (Tm)-doped and thulium-holimium (Tm-Ho) co-doped fiber lasers are useful and promising laser sources at this wavelength range. Passively mode-locked ultrashort pulse fiber lasers have been demonstrated, using nonlinear polarization rotation (NPR) and a semiconductor saturable absorber mirror (SESAM) [1, 2, 3, 4, 5]. Since there are water vapor absorptions in this wavelength range, an all-fiber configuration is preferred to realize stable operation. Using nano-carbon materials, an all-fiber type ultrashort pulse fiber laser can easily be demonstrated.  
The first demonstration of a Tm-doped fiber laser using a single walled carbon nanotube (SWNT) film was reported in 2008 [6]. Then, a dispersion management and wavelength tuning operation were reported by some groups [7, 8, 9, 10, 11]. Passively mode-locked ultrashort pulse Tm-doped fiber lasers using graphene devices in a similar manner have also been demonstrated [12, 13, 14].  
In our previous work, we demonstrated that ultrashort Tm-doped fiber laser using SWNT dispersed in polyimide film [15]. SWNTs with an almost constant diameter of 1.6 nm were synthesized with the enhanced direct injection pyrolytic synthesis (e-DIPs) method, and thin polyimide film devices dispersed with SWNT were developed as the saturable absorber [16]. Stable soliton mode-locking, and high-power dissipative soliton mode-locking operation were achieved successfully.  
One of the problems with the Tm-doped fiber laser is its low pumping efficiency. In our previous work using the Tm-doped fiber, the pumping efficiency from the pump laser to fiber laser output was \(\sim\)5%, and this requires improvement. The Tm-Ho co-doped fiber has wide-gain bandwidth and, thanks to the co-doping, high pumping efficiency is expected. So far, although there have been a few reports about a Tm-Ho co-doped fiber laser, power scaling, ultrashort pulse generation, and wavelength control are still desired [5; 17; 18; 19].  
In this work, we demonstrated a dispersion-manage, Tm-Ho co-doped ultrashort pulse fiber laser using SWNT polyimide film. In terms of its applications for biomedical imaging, control of the oscillation wavelength is required in order to achieve high gain in the following Tm-doped fiber amplifier [20]. In this work, an in-line tunable spectral filter was developed and applied for spectral control. The spectral filter is effective in suppressing the accompanying sidebands in a mode-locked pulse. It is also effective in achieving stable passive mode-locking. Two SWNT polyimide films with different absorption and modulation depths were examined as the mode-locker. The net cavity dispersion was varied from negative to positive, and the characteristics of output pulses were precisely investigated.
## 4 Materials and Methods
### Performance Metrics  
In this study, ROUGE is used to evaluate the summarization performance by comparing model-generated summaries with human-written references. ROUGE measures the overlap of textual units such as n-grams and word sequences to assess the quality of generated summaries. This paper considers several ROUGE variants, including ROUGE-N, ROUGE-L, ROUGE-W, and ROUGE-S. Widely adopted in NLP tasks like summarization, machine translation, and text generation, ROUGE offers a practical and comparative metric. Its primary goal is to assess how accurately and comprehensively the model captures the content of the reference text. While ROUGE only approximates semantic similarity, it provides a cost-effective alternative to manual evaluation. It reports a single performance score using Precision (P), Recall (R), and the F-measure (F). Precision measures the proportion of relevant units in the candidate summary, while Recall assesses how much of the reference is captured. The F-measure balances P and R using the \(\beta\) parameter. These metrics take values between 0 and 1 and are calculated as follows,  
\[\text{R}=\frac{\text{correct sentences in the candidate summary}}{\text{total number of sentences in the model summary}} \tag{1}\]  
\[\text{P}=\frac{\text{correct sentences in the candidate summary}}{\text{total sentences in candidate summary}} \tag{2}\]  
\[\text{F}-\text{\emph{measure}}=\frac{\left(1+\beta^{2}\right)\text{RP}}{ \text{R}+\beta^{2}\text{P}} \tag{3}\]  
ROUGE-N specifically measures n-gram overlap, where N represents the n-gram length. If the candidate summary is denoted by s, ROUGE-N is computed as  
\[\text{ROUGE}-\text{N}=\frac{\sum_{r\in M}\langle\Phi\text{N}(\text{r}),\Phi \text{N}(\text{s})\rangle}{\sum_{r\in M}\langle\Phi\text{N}(\text{r}),\Phi \text{N}(\text{r})\rangle} \tag{4}\]  
\(\Phi\)N(d) is the binary vector representing the N-grams in document d. Here, the ith component of the vector takes the value 1 if the ith N-gram is in d, and it is 0 otherwise. Here, () are the inner products of the vectors. ROUGE-L calculates the longest substrings in common between the candidate and the reference summary. ROUGE-W uses a weighting factor for consecutively overlapping words. It searches for strings with consecutive matches. ROUGE-W is a variant of ROUGE-L. ROUGE-S is a skip-bigram co-occurrence. ROUGE-SU is its average [29; 61].
# FETrack: Feature-Enhanced Transformer Network for Visual Object Tracking
## 4 Experiments  
### Implementation Details  
**Model.** We utilize the native ViT-Base model, pre-trained with MAE on ImageNet, as the backbone of our FETrack encoder. The number N of encoders is 12, the size of the encoder hidden is 768, and the number of attention heads is 12. The resolutions of the template and the search images are 128 \(\times\) 128 and 256 \(\times\) 256, respectively. All input images  
Figure 4: Schematic diagram of the proposed template-filtering (TF) approach. The TF reduces the background noise, enabling the template image to focus more on the target.  
are split into numerous patches of size 16 \(\times\) 16. The decoder consists of two transformer blocks, the size of the decoder hidden is 256, and the number of attention heads is 8. The word-to-embedding is a linear projection layer with an embedding dimension of 256, which is consistent with the size of the hidden layer of the decoder. The embedding-to-word sampling is a three-layer perceptron connected with a softmax. The hidden dimension of the perceptron is 256, and the output dimension is 4000. Finally, the word with the maximum likelihood is sampled as the output word. To verify the flexibility of the proposed FETrack, we also test a high-resolution variant with the template and the search images of size 192 \(\times\) 192 and 384 \(\times\) 384, respectively. Our models are trained on a GeForce RTX3090 24 GB GPU (NVIDIA Corporation, Santa Clara, CA, USA). All models are implemented using Python 3.8 and PyTorch 1.11.0.  
**Training.** Our training data cover four datasets, namely GOT-10k [17], TrackingNet [19], COCO [40], and LaSOT [18]. Considering the VOT2020 evaluation protocol [41], we remove 1k forbidden videos in GOT-10k during training. For the evaluation of the GOT-10k test set, we follow the official one-shot protocol and only use the training split of GOT-10k for training. The template and the search images are obtained by expanding the bounding box by two and four times. The detailed training settings are shown in Table 1.  
**Inference.** In the inference stage, we adopt the proposed dynamic threshold-based online template-updating strategy to update the dynamic template. Specifically, the dynamic threshold \(\sigma_{1}\) is initialized to one to avoid unnecessary template updates in the initial stage. The thresholds \(\sigma_{i}\) in subsequent stages are determined by the confidence score \(s_{i}\) of the current frame and the threshold \(\sigma_{i-1}\) of the previous frame, and \(i\) starts from two. For the window penalty, we impose penalties on the predicted coordinate values to suppress the large displacement of the target between consecutive frames. Specifically, the output of the predicted coordinates after the embedding-to-word module is a softmax score vector of size 4000, which is then multiplied by a 1-D Hanning window of size 4000 to implement the window penalty.
# Nonlinear Optimization and Adaptive Heuristics for Solving Irregular Object Packing Problems
## 5 Further Scalable Object Configuration Problems
### General Sphere Packings in \(R^{d}\)  
This model type is a direct generalization of the GCP problem for arbitrary dimension \(d>2\). Given a finite collection of \(d\)-dimensional spheres, our goal is to find the smallest sphere in \(R^{d}\) that contains the given spheres in a non-overlapping arrangement. For \(i=1,\ldots,N\), let \(S_{i}\subset R^{d}\) denote a \(d\)-sphere with radius \(r_{i}>0\). Let \(S_{0}\) denote the container \(d\)-sphere with radius \(r_{0}\). Similarly to the GCP model, we set \(c_{0}=0\in R^{d}\) as the center of the container sphere and denote the (to be optimized) center position of sphere \(S_{i}\) by \(c_{i}=\left\{x_{i,1},\ x_{i,2},\ldots,\ x_{i,d}\right\}\). The Euclidean distance between the pair of \(d\)-sphere centers \(c_{i}\) and \(c_{j}\) is denoted by \(e_{ij}=||c_{i}-c_{j}||\), where \(||c_{i}||=\sqrt{\sum_{k=1}^{d}x_{i,k}^{2}}\).  
Applying this notation, the general sphere packing (GSP) model is formulated as follows.  
* Minimize \(r_{0}\)
* \(||c||^{2}\leq(r_{0}-r_{i})^{2}\) for \(i=1,\ldots,N\)
* \((r_{i}+r_{j})^{2}\leq e_{ij}^{2}\) for \(i,j=1,\ldots,N,i<j\).  
To illustrate the numerical solution of GSP instances, below we present optimized sphere configuration results with up to \(N=50\) spheres with radii \(r_{i}\) for \(i=1,\ldots,N\) in dimension \(d=3\). In our study, we used _Mathematica_ and LGO linked to _Mathematica_ on one of our personal computers. For comparison, we also present results using a hybrid heuristic optimization approach. For further details, consult [PERSON] et al. [55].  
For \(d=2\), our numerical results are, on average, within \(1\%\) of the entire set of best-known results for the Zimmermann [38] model instances in \(R^{2}\). We found new (conjectured) sphere packings for previously unexplored generalizations of the same model class in \(R^{d}\) with \(d=3,4,5\), as can be seen in the examples in Table 2. As an example, Figure 2 displays the numerically optimized 10-sphere instance in \(R^{3}\).  
Without going into further details, let us point out that--similarly to generalizing GCP to GSP--all three model classes considered next can be directly generalized to arbitrary dimension \(d>2\), with corresponding objects and containers defined in \(R^{d}\).
## 6 Lamb Waves and Shear Horizontal Waves
### A0 Lamb Wave Mode  
The six eigensolutions at a frequency of \(3.188\times 10^{6}\) rad/s and a wavenumber of \(1688.7\) rad/m are shown in Table 5. This frequency-wavenumber pair corresponds with the red-triangle symbol plotted in the dispersion-curve plot shown in Figure 2. Similar to the Christoffel equation solutions used for the Rayleigh -wave case, the solutions in Table 5 are typical of Region 1 (Figure 1a), in which the A0 mode exists. Unlike the Rayleigh wave case, there is now a bottom surface, so the exponentially increasing eigensolutions are now necessary, although they will be written as exponentially decreasing from the bottom surface upward as \(e^{-ik_{z}(z-H)}\). Using the coordinate system shown in Figure 6, assignment to the top or bottom of the plate means that the partial waves will be defined using \(e^{-ik_{z}(z)}\) or \(e^{-ik_{z}(z-H)}\), respectively. For simplicity, the rule used in this paper to categorize the partial waves between the top or bottom of the plate is shown in Table 6.  
Figure 5: Wave structure of a Stoneley wave calculated using the partial-wave method at an aluminum/tungsten interface. Blue lines are the coordinate axis. \(-z\) is in the tungsten half-space, and \(+z\) is in the aluminum half-space.  
The superposed displacement profile in the \(z\)-direction is constructed by considering the symmetry of the problem:  
\[\begin{split} u_{i}(z)=B^{(+L)}u_{i}^{(+L)}e^{-\hat{u}_{z}^{(+L)}(z-H )}+B^{(-L)}u_{i}^{(-L)}e^{-\hat{u}_{z}^{(-L)}z}+B^{(+SV)}u_{i}^{(+SV)}e^{-\hat{u} _{z}^{(+SV)}(z-H)}+\\ B^{(-SV)}u_{i}^{(-SV)}e^{-\hat{u}_{z}^{(-SV)}z},\end{split} \tag{38}\]  
where \(u_{i}(x,z,t)=u_{i}(z)e^{i(-k_{x}x+\omega t)}\). For partial waves existing on the bottom surface of the plate, it is shown in Equation (38) that the \(z\)-position is offset by the thickness of the plate. Just as in the Rayleigh wave problem, \(B^{(-L)}\), \(B^{(+SV)}\), and \(B^{(-SV)}\) can be solved by using three of the four BC available (assuming that \(B^{(+L)}=1\)):  
\[\sigma_{zz}=0\text{at}z=0, \tag{39}\]  
\[\sigma_{xz}=0\text{at}z=0, \tag{40}\]  
\[\sigma_{zz}=0\text{at}z=H, \tag{41}\]  
\[\sigma_{xz}=0\text{at}z=H, \tag{42}\]  
It was found that \(B^{(-L)}=1\), and \(B^{(+SV)}=B^{(-SV)}=-1.0284\). As before, Equation (38) can now be used to plot the wave-structure of the A0 mode at this frequency.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{5}{c}{**Top of Plate**} & **Bottom of Plate** \\ \hline Real-valued (bulk waves) & \(\mathbf{k}_{i}^{(+L)}\) and \(\mathbf{k}_{i}^{(+SV)}\) & \(\mathbf{k}_{i}^{(-L)}\) and \(\mathbf{k}_{i}^{(-SV)}\) \\ Imaginary-valued (surface waves) & \(\mathbf{k}_{i}^{(-L)}\) and \(\mathbf{k}_{i}^{(-SV)}\) & \(\mathbf{k}_{i}^{(+L)}\) and \(\mathbf{k}_{i}^{(+SV)}\) \\ \hline \hline \end{tabular}
\end{table}
Table 6: Method for assigning partial waves to the top or bottom of the plate using the coordinate system shown in Figure 6 and assuming \(e^{-\hat{\mathbf{u}}_{z}z}\).  
Figure 6: Conceptual depiction of the partial waves, which are necessary for the A0 Lamb wave problem, overlaid on a plate in vacuum.
## 4 Evaluation
### Recognition Approach  
Since the aim of this work is not to optimize the recognition of a specific set of activities but to evaluate the usefulness of simulated sensor data generated from video for wearable HAR systems in general, we use a standard architecture that has proven to be well suited for a variety of activity recognition tasks. The full network architecture can be seen in Figure 8, and, just like the regression model, is based on [42]. The input to the recognition system is 2.56 s (128 frames) long windows. This window size is sufficient to capture the motions of each exercise, which is repeated many times in a span of 30 s. Since all convolutions in this architecture use _same_ padding, the output class predictions are on frame, not on window level. This allows the network to predict with finer granularity and better deal with cases where two activities are happening inside the same window (transition between activities). For the final prediction we apply majority voting to each window. We trained the network using the categorical cross-entropy loss function and the Adam optimizer [44] with 0.001 learning rate and 0.9 and 0.999 for \(\beta_{1}\) and \(\beta_{2}\), respectively. Each model is trained for 500 epochs with early stopping using a patience of 25 to avoid overfitting. If any real (not simulated) sensor data is included in a test, a stratified 10% random selection of it is used as the validation set. If no real data is included, the validation set consists of the same selection strategy but applied to all the simulated data. This guarantees the quality of the validation set, while avoiding unfair advantage to configurations that combine simulated and real data. If we simply selected a stratified 10% of all data for validation, tests that combine data (simulated and real) could benefit from having more real points in the training set.
# An Adjusted Frequency-Domain Algorithm for Arc Array Bistatic SAR Data with One-Moving Transmitter
## 4 Resolution Analysis  
The spatial resolution is one of the important indicators to evaluate the effectiveness of radar imaging algorithms, which reflects the ability of radar to distinguish targets in different locations. The azimuth resolution of AA-BiSAR is mainly determined by the arc synthetic aperture and the relative position between the receiver and target. The range resolution is mainly achieved by the transmission bandwidth signal. In this paper, the spatial resolution of AA-BiSAR with the moving transmitter is analyzed in detail, and the spatial resolution of the moving receiver is studied.
# Performance Optimization of Object Tracking Algorithms in OpenCV on GPUs
## 4 Performance Optimization of the OpenCV Object Tracking Algorithm
### Improving the Occupancy  
The occupancy is the most effective metric for measuring the resource utilization of GPU hardware [19, 40]. Therefore, increasing the occupancy generally leads to improving the kernel's performance [40]. In particular, optimizing the kernel can significantly improve the application's overall performance when a specific kernel accounts for a high execution time ratio. This section presents optimization schemes to increase the parallelism, thereby increasing the occupancy, of the kernels, which accounts for the largest fraction in the execution time of the object detection algorithm.
# REDUCE--A Tool Supporting Inconsistencies Reduction in the Decision-Making Process
###### Abstract  
This paper presents REDUCE, a free online tool designed to support decision-making processes by addressing inconsistency in multiplicative pairwise comparison (PC) matrices, a key element of many multi-criteria decision-making (MCDM) methods, including the analytic hierarchy process (AHP). AHP relies on pairwise comparisons to assign weights to decision criteria or alternatives, but human-generated PC matrices often exhibit inconsistencies. Consistency is evaluated using Saaty's consistency ratio (\(CR\)), where a value below 0.10 is considered acceptable. Higher inconsistency levels necessitate matrix corrections, which are challenging if the original expert is unavailable or revision constraints exist. REDUCE autonomously reduces inconsistency in PC matrices using two different algorithms that require no expert intervention. The tool accommodates different PC matrices, enabling users to specify the desired CR threshold (e.g., \(CR\leq 0.10\)) and select the algorithm for adjustment. It ensures the resulting matrix is consistent while preserving the original preference structure to the greatest extent possible. Additionally, REDUCE calculates weights for the compared entities, making it a valuable tool for applications of AHP and related methodologies. Quantitative evaluations demonstrate that REDUCE can improve matrices with high inconsistency (e.g., \(CR=0.25\)) to acceptable levels (e.g., \(CR=0.08\)) while retaining up to 95% of the original preference integrity, depending on the chosen algorithm. By addressing the accessibility gap for small and medium enterprises (SMEs) that lack resources for costly decision-making software or expert consultants, REDUCE facilitates broader adoption of MCDM tools. This work highlights the potential of REDUCE to enhance decision-making reliability and accessibility in resource-constrained environments.  
decision support inconsistency reduction; pairwise comparisons; small/medium enterprises Article
## 1 Introduction
[34] examined postural variability during self-perceived comfortable sitting on three chair types: a stool, computer chair, and gaming chair. Despite standardized instructions, participants showed high intra-individual variability in global sagittal angles: 9.4\({}^{\circ}\) (head), 10.2\({}^{\circ}\) (trunk), and 11.1\({}^{\circ}\) (knee). These findings suggest that even comfortable postures are not consistently reproducible. Gender and the chair type significantly influenced trunk flexion but had no effect on head or knee angles. In a related study, [PERSON] et al. [11] found that participants shifted to more upright postures when asked to sit \"optimally,\" with women demonstrating greater extension in cervical, thoracic, lumbar, and pelvic segments--possibly due to anatomical or perceptual differences. While these studies offer insights into comfortable and habitual postures, few have compared multiple sitting strategies--comfortable, habitual, and correct--across various chair types. Moreover, little is known about the reproducibility of these postures under repeated conditions. This is particularly relevant given the increasing use of memory based seat adjustment systems in office chairs and vehicles, which assume fixed, preferred postures can be accurately reinstated over time. To address these gaps, the current study investigates postural variability in global joint angles--head inclination (HI), trunk angle (TA), and knee angle (KA)--across three chair types (stool, computer chair, ergonomic chair), across three sitting strategies (comfortable, habitual, correct), and between genders. Building on [PERSON] et al. 's work [34], this study systematically examines whether individuals adopt consistent postures across repetitions, how chair design and gender influence posture, and whether certain strategies (e.g., correct sitting) yield lower variability. We hypothesize that (1) correct sitting will show significantly lower postural variability than comfortable and habitual postures due to greater intentional control; (2) gender will affect TAs, with females exhibiting more extended postures; (3) the chair type will influence variability, with adjustable chairs increasing variability in comfortable sitting but reducing it in correct sitting; and (4) postural variability will exceed minimal detectable change thresholds across conditions, challenging assumptions of posture reproducibility. These findings aim to inform ergonomic chair design, postural training, and individualized seating recommendations in sedentary environments.