esa-sceva/satcom-chunk-collection huggingface dataset:
## Abstract  
The aims of this study were to assess the common anomalies in the MRI examinations of the heads of soldiers as well as to compare the relative concentration of magnetic resonance spectroscopy (MRS) metabolites in the brains of soldiers with those of healthy age-matched controls. Overall, 54 professional male soldiers were included in the study group and 46 healthy, age-matched males were in the control group. The relative values of N-acetylaspartate (NAA), choline (Cho), and myoinositol (ml) to creatine (Cr) were assessed. The mean relative concentrations of metabolites were compared between the study and the control group, separately for the frontal and occipital lobes, as well as between the right and left hemispheres within the study group only. The most frequent findings in the head MRI of the soldiers were: asymmetric lateral ventricles and dilated perivascular spaces, enlargement of the subarachnoid spaces, and the presence of cavum septum pellucidum and cavum vergae; the high frequency of sinus disease should also be noted. In the frontal lobes, the ml/Cr ratio was significantly higher (\(p\) = 0.005), while the NAA/Cr ratio was lower (\(p\) = 0.001), in the group of soldiers (vs. the study group). In the occipital lobes, the NAA/Cr ratio was significantly lower (\(p\) = 0.005) in the military personnel and there was a tendency to a higher ml/Cr ratio in the soldiers' occipital lobes (\(p\) = 0.056) (vs. the study group). Comparing the metabolites between the left and right hemispheres in soldiers preferring a right shooting position, a significantly higher ml/Cr (\(p\) < 0.001) ratio was observed in the right frontal lobe (vs. the left) and a markedly lower NAA/Cr (\(p\) = 0.003) in the right occipital lobe (vs. the left). These changes are associated with astrogliosis and neuronal loss, presumably secondary to repetitive mild traumatic brain injury.  
1
# Earthquake Magnitude and Frequency Forecasting in Northeastern Algeria using Time Series Analysis
## 3 Methodology Outline
\right) \tag{10}\]  
The window length \(L\) is obtained through experimentation, and the appropriate \(L\) parameter is determined according to  
with  
\[X_{i}=\sqrt{\lambda_{i}}U_{i}V_{i}^{T} \tag{13}\]  
The matrices \(X_{i}\), called elementary matrices, with the rank equal to one, and the set of triplets \(\left(\sqrt{\lambda_{i}},U_{i},V_{i}\right)\) are called the \(i\)th eigen-triplet of the singular values decomposition. The second stage of the algorithm implementation corresponding to the reconstruction stage includes two other steps: the eigentriple grouping and the diagonal averaging steps. The eigentriple grouping step consists of dividing the elementary matrices \(X_{i}\) (\(i=1,\ldots,L\)) into \(r\) groups, \(1\leq r\leq d\)[94]. The resulting matrices are then produced by adding the \(r\) eigen-triples in each group. Whereas, the diagonal averaging step is based on the reconstruction of the one-dimensional series of length \(N\), that can be considered as an approximation of the original series, by applying the diagonal averaging method on the grouped matrices that are resulted in the previous step [93; 96]. In order to compare between the different approaches applied, an important convention is widely used and based on the root mean square error (RMSE) criterion [38; 39], which is calculated by  
\[RMSE=\sqrt{\frac{1}{N}\sum_{k=1}^{N}(x_{k}-\hat{x}_{k})^{2}} \tag{14}\]  
where \(x_{k}\) indicates the actual value and \(\hat{x}_{k}\) represents the \(k\)th forecasted value based on the previous data. Therefore, the optimum model is the one presenting the smallest RMSE value.
## 5 Object Detection Application Domains
### Medical Image Analysis  
In medical image analysis specifically, tumor detection, skin disease detection, tumor segmentation, brain image analysis, glaucoma detection, healthcare monitoring, etc., are the major fields to apply deep learning-based techniques. In the medical field, detection is commonly referred to as \"computer-aided detection (CAD)\". CAD systems aim to detect the abnormalities in a patient as early as possibly; for instance, in cases of breast cancer or lung cancer.  
[PERSON] et al. [173] implemented \"two-deep CNN networks Inception v4 [174] and ResNet [39] to detect Alzheimer's disease using MRI images\" of OASIS dataset [175]. The \"Deep CNN model [was] proposed for epilepsy lesion detection in multiparametric MRI images using auto-encoders\" [176]. [PERSON] et al. [177] proposed a \"multi-parametric deep CNN model for meningiomas detection\" in the brain and a \"deep CNN technique for the estimation of colorectal cancer in CT tumor images\" for early treatment [178].  
[PERSON] et al. [179] proposed \"deep CNN techniques for metastases detection in hematoxylin tissue sections of the lymph nodes\" subjected to cancer. A \"fully-convolutional network-based heat regression method [was] implemented for the detection of breast mass\" in mammography images [180].  
A \"CAD system based on a deep CNN model to detect breast cancer\" in MRI images was proposed in [181].  
[PERSON] et al. [182] proposed the \"CNN technique to detect diabetic retinopathy in fundus images\" using public datasets. A \"3D group-equivariant CNN technique for lung nodule detection\" in CT images was proposed in [183]. Recently, deep learning-based techniques have been used for diagnosing retinal diseases [184; 185]. [PERSON] et al. [177] introduced a CNN-based attention mechanism for glaucoma detection. A deep CNN model [186] was introduced for melanoma detection, and there was also a \"Deep CNN for the detection of chronic obstructive pulmonary disease (COPD) and acute respiratory disease (ARD) prediction\" in CT images of smokers [187].  
The widely used datasets for evaluating medical image analysis performance are different for different diseases. ILD [188] and LIDC-IDRI [189] datasets for the lung; ADNI [190], BRATS [191], and OASIS-3 [175; 192] datasets for the brain; DDSM [193], MIAS [194], CAMELYON 17 [195], and INbreast [196] datasets for the breast; DRIVE [197], STARE [198], and MESSIDOR-2 [199] datasets for the eye.  
The major challenge faced in the medical imaging field is the imbalance of samples in available datasets, so there is a need to develop large-scale medical imaging datasets. The best solution is to apply multi-task learning on the deep neural network when the training data are scarce. The other possible solution is applying data augmentation techniques to the images.
## 3 Results
### Microbiome Analysis
#### 3.4.1 Taxonomy Assignment
The relative frequencies of Proteobacteria, Firmicutes, Bacteroidetes, Actinobacteria, Fusobacteria, Spirochaetes, TM7, and Synergistetes were found to be \(\mu=86.91\%\), \(\mu=10.37\%\), \(\mu=1.23\%\), \(\mu=0.81\%\), \(\mu=0.2\%\), \(\mu=0.21\%\), \(\mu=0.17\%\), and \(\mu=0.03\%\), respectively, in G1 samples before treatment. Likewise, the relative frequency of Proteobacteria, Firmicutes, Bacteroidetes, Actinobacteria, Fusobacteria, Smyrochaetes, TM7, and Synergistetes were \(\mu=22.52\%\), \(\mu=36.01\%\), \(\mu=28.03\%\), \(\mu=6.24\%\), \(\mu=3.62\%\), \(\mu=1.43\%\), \(\mu=1.27\%\), and \(\mu=0.18\%\), respectively, in G1 samples after treatment with DMW (Figure 4). Similarly, the relative frequency of phyla was changed in G2 samples after treatment compared to pre-treated samples, as follows: Proteobacteria (from \(\mu=81.71\) to \(\mu=26.98\%\)), Firmicutes (from \(\mu=14.30\) to \(\mu=41.44\%\)), Bacteroidetes (from \(\mu=1.94\) to \(\mu=17.72\%\)), Actinobacteria (from \(\mu=1.29\) to \(\mu=8.49\%\)), Fusobacteria (from \(\mu=0.33\) to \(\mu=3.90\%\)), TM7 (from \(\mu=0.36\) to \(\mu=1.04\%\)), SR1 (from \(\mu=0.02\) to \(\mu=0.32\%\)), and Spirochaetes (from \(\mu=0.05\%\) to \(\mu=0.07\%\)) (Figure 5). Other minor changes in the microbial abundances for DMW and DNMW are detailed in Figure S1a,b (Supplementary File S1). Klebsiella (20.733%), Citobacter (11.615%), Chromobacterium (10.849%), Achromobacter (9.381%), Stentrophomonas (7.601%), Serratia (7.304%), Shewanella (2.723%), Morganella (0.591%), Agrobacteriumium (0.502%), Staphylococcus (0.328%), Desulfovich (0.300%), Lactococcus (0.296%), Delftia (0.251%), and Corynebacterium (0.106%) were found in pre-treatment G1 samples (Figure S2) (Supplementary File S1). _Pseudomonas_ (22.881%), _Stenotrophomonas_ (17.370%), _Achromobacter_ (7.020%), _Serratia_ (3.552%), _Brecurudomonas_ (1.587%), _Klebsiella_ (0.737%), _Agrobacteriumium_ (0.634%), _Lactobacillus_ (0.127%), and _Treponema_ (0.048%) were found in pre-treatment G2 samples (Figure S3) (Supplementary File S1). After the use of the mouthwashes, the genera mentioned above were completely eradicated in both G1 and G2 samples. Figure 3: Taxonomy was assigned to the samples (pre-and post-treatment) collected from G1 (**a**) and G2 (**b**), and its relative frequency was compared. Figure 4: The abundant phyla of the G1 (mouthwash formulation I) samples were compared before and after mouthwash to identify the formulation’s efficacy. Red and blue bars indicate the pre-and post-treatment samples, respectively.
# A Task-Centric Cooperative Sensing Scheme for Mobile Crowdsourcing Systems
## 3 Problem Statements
### Estimating Sensing Data Using Spatial Correlation
For convenience, let us denote \(\mathbf{Data^{\prime}}_{R}\) to be the sensing measurements corresponding to grid indices _I_\({}_{R}\) during normal operation; and \(\hat{\textbf{Y}}_{K-R}\) to be the estimated measurements corresponding to those idle participants using \(\mathbf{Data^{\prime}}_{R}\). Since \(\mathbf{Data^{\prime}}_{M-R}\) is unavailable, our approach is to use the **B** matrix obtained using the training data to estimate\(\mathbf{Data^{\prime}}_{M-R}\):  
\[\mathbf{Data^{\prime}}_{M-R}=\mathbf{Data^{\prime}}_{R}*\textbf{B} \tag{6}\]  
The implicit assumption we make here is that the spatial correlations estimated during the training period would remain the same during the subsequent normal operation. To account forslow time variation of the spatial correlation, retraining of the MCS may be performed periodically at pre-scheduled intervals.
# Simulative Investigation of the Radar Cross Section of Wind Turbines
## 5 Conclusions
### Further Work  
In order to more rapidly predict radar backscattering of wind-turbine rotors, research should be undertaken to derive a simplified model. This model should be able to compute the RCS from the used airfoil profiles, their length along the rotor blade, and the angle of incidence. These models will need to be verified with model-scaled laboratory measurements.  
Further research in improved signal processing algorithms for weather radar data should also be considered.  
The topic of influence on Doppler radar systems has not been discussed in this paper. However, it deserves mention. A feasible research approach could be model-scaled measurements at higher frequencies (beyond 100 GHz), in a rotating setup. Given the trend towards CRP, metal-coated 3-D printed rotor blades could be used.  
Conceptualization, S.H. and D.P.; Methodology, S.H. and D.P.; Validation, S.H. and D.P.; Investigation, S.H. and D.P.; Data Curation, S.H.; Writing-Original Draft Preparation, S.H.; Writing-Review & Editing, S.H. and D.P.; Visualization, S.H.; Funding Acquisition, D.P.  
This research was funded by the German Federal Ministry for Economic Affairs and Energy (\"Bundesministerium fur Wirtschaft und Energie\") under grant number 0325727B. Open Access Funding by the Publication Fund of the TU Dresden.  
Simulations were performed on the \"taurus\" cluster at the Center for Information Services and High Performance Computing (ZIH) at TU Dresden. We thank our colleagues at the Institute of Textile Machinery and High Performance Material Technology for providing a CAD model of an investigated rotor blade (blacke v3).  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} CAD & computer-aided design \\ CRP & carbon-reinforced plastic \\ DU & Delft University \\ FRP & fiber-reinforced plastic \\ NACA & National Advisory Committee for Aeronautics, predecessor of NASA \\ RCS & radar cross section \\ RF & radio frequency \\ WT & wind turbine \\ \end{tabular}
## 4 Security Analysis
### Informal analysis
#### 4.2.9 Desynchronization Attack  
Desynchronization attack means that attackers block or intercept the interaction of the subject during the key update phase so that one party completes the key update and the other fails to do so. In this protocol, each legitimate entity is authenticated to perform the key update, and the key update is performed by passing the authentication, as well as determining whether the message is truncated and retransmitted by the freshness of the timestamp. Therefore, this protocol is resistant to desynchronization attacks.
# A Survey on Hardware Accelerators for Large Language Models
## 4 CPU- and GPU-Based Accelerators
### Llma  
In 2023, Microsoft presented LLMA [22], an LLM accelerator that is used to speed up large language model (LLM) inference with references. LLMA was mainly motivated by the observation that there are many identical text spans between the decoding result by an LLM and the reference that is available in many real-world scenarios (e.g., retrieved documents). LLMA first selects a text span from the reference and then copies its tokens to the decoder. It then evaluates the tokens' appropriateness as the decoding results in parallel within a single decoding step. This improved computational parallelism enables LLMA to achieve over a 2\(\times\) speedup for large language models (LLMs) with generation results identical to those of greedy decoding. However, this speedup is achieved only in use cases where there is significant overlap between in-context references and outputs, such as in search engines and multi-turn conversations.  
The main advantage of LLMA, compared to other efficient decoding algorithms such as speculative decoding [23] and speculative sampling [24] is significant., While these methods require an additional efficient drafter model to generate a draft for checking, LLMA does not require an additional model and is much easier to implement and deploy.  
For the performance evaluation, the authors used the LLMA model with 7 B, 13 B, and 30 B parameters, and all the inferences were conducted in half-floating numbers. For the 7 B and 13 B models, the inferences were performed on one NVIDIA 32 GB V100 GPU, and for the 30 B model, the inference was performed on four NVIDIA 32 GB V100 GPUs on a single machine.  
The improved computational parallelism enabled LLMA to achieve a 2\(\times\) to 3\(\times\) speedup over the baseline for LLMs, with generation results identical to those of greedy decoding in many practical scenarios, where significant overlap between in-context reference and outputs existed (e.g., search engines and multi-turn conversations).
# COMS Visible Channel Calibration Using Moon Observation Data
## Appendix A
### Phase Angle Dependence  
The results presented in Figures 3 and 4 show periodicity in changes of the degradation ratio (P(t)) for the COMS MI visible channel. Moon irradiance caused errors with respect to the Moon's absolute phase angle. The phase angles of lunar images observed from COMS MI had similar patterns every year, which created seasonal variation in the irradiance ratio.  
The degradation tendency of the visible channel of COMS MI was analyzed by dividing it into three regions according to the phase angle range. This allowed an observation of changes in the degradation ratio according to the Moon phase angle. Table 11 shows Moon observation images and phase angles in the three ranges of absolute phase angles.  
Figure 12 shows the attenuation tendency of the COMS MI visible channel according to the phase angle range of the Moon.  
As shown in Figure 12, the degradation ratio tendency variesd depending on the absolute phase angle range.  
In Table 13, the degradation ratio was the same when the absolute phase angle was less than \(30^{\circ}\), although there are differences of approximately of 0-8% with a Moon degradation ratio of \(-1.35\%\) per year, with respect to the result of Equation (9) using a full Moon image.
## 2 Materials and Methods
### Analysis of the Obtained Data Using a Reference Best-Fit-Alignment  
The captured 3D data were analyzed with Geomagic Qualify 2012 (V.2012_08_08_E, 64-bit, Morrisville, NC, USA). The files were imported as STL (Standard triangulation language) datasets. The surface of the obtained datasets consisted of a triangle mesh. In this mesh, one triangle adjoined other triangles, creating a vertex at each meeting point.  
First, each dataset (t1-t3) was superimposed with its respective baseline data file. Before superimposition, the worn areas were virtually excluded in the software to achieve a more accurate best fit alignment (tolerance value 0.01 mm [8]) [12; 13; 14].  
Thus as an example, OC p \(\mathrm{t}\mathrm{/}_{\mathrm{n(1-6)}}\) was defined as a reference and registered with OC p \(\mathrm{t}\mathrm{/}_{\mathrm{n(1-6)}}\), \(\mathrm{t}\mathrm{2}\mathrm{/}_{\mathrm{n(1-6)}}\), and \(\mathrm{t}\mathrm{3}\mathrm{/}_{\mathrm{n(1-6)}}\). The superposition of the datasets required the definition of a reference and a test dataset. The reference t0 was fixed in the coordinate system. Therefore, the test dataset t1-t3 could be positioned as identically as possible. The WLP was the key dataset, which determined the surface area to be superimposed since it has a smaller field of view than the IOS. To ensure comparability, the same area size was therefore included in the registration.  
Figure 3A shows the result of the superimposition of two meshes according to best fit alignment. The bluish color-coded area indicates the wear facet between the baseline t0 and a stage of wear. The schematic drawing in Figure 3B displays the distance measurement principle between two datasets: a perpendicular was dropped from one vertex of the baseline dataset to a corresponding triangular facet that belongs to another dataset where a wear facet was simulated.  
The maximum wear rate was determined manually. Therefore, each value could be analyzed, avoiding outliers such as punctual peaks which could be identified as artifacts. The color code, given in a 3D comparison mode, was modified until the maximum loss was indicated. Applying this scale mode, it was possible to check if the area of maximum wear was located in the corresponding area of different IOS and WLP. As six datasets of each wear status were available, the mean maximum vertical height loss and the standard deviation of each group (OC non-p, OC p, Tr3 non-p, Tr3 p, TD) was calculated with Excel 2016. Divergences from the reference measurements obtained from WLP were expressed in \(\upmu\)m and as percentage deviations.  
Figure 2: The study model was digitized and imported into Geomagic Qualify. The several wear steps are presented: Baseline t0 (**A**) and the wear stages [1] (**B**), t2 (**C**), and t3 (**D**).
## 4 Branch-and-Price-and-Cut Algorithm
### Label Setting Algorithm
#### 4.4.3 Dominance Rules  
In essence, the label setting algorithm shares similarities with enumeration methods and dynamic programming, as it systematically considers all feasible extensions for each label. Nevertheless, as the label setting algorithm progresses through extensions, the generated labels can grow at an exponentially increasing rate. To counteract this exponential growth and enhance computational efficiency, dominance rules are introduced. These rules serve as strategic filters, judiciously discarding unpromising labels, thereby allowing the algorithm to concentrate its efforts on the most promising labels.  
Given two labels \(L_{i}^{1}=\{i,y_{i}^{1},AC_{i}^{1},RC_{i}^{1},V_{i}^{1}\}\) and \(L_{i}^{2}=\{i,y_{i}^{2},AC_{i}^{2},RC_{i}^{2},V_{i}^{2}\}\), which are different partial routes that reach the same node \(i\), label \(L_{i}^{1}\) dominates label \(L_{i}^{2}\) if the following rules are met: (i) \(y_{i}^{1}=y_{i}^{2}\); (ii) \(AC_{i}^{1}\leq AC_{i}^{2}\); (iii) \(RC_{i}^{1}\leq RC_{i}^{2}\); (iv) \(V_{i}^{1}\leq V_{i}^{2}\). We can identify the critical time \(CT_{t}\) after which all subsequent time points are associated with dual variable values of zero. Then, dominance rule (i) can be changed into \(y_{i}^{1}\leq y_{i}^{2}\) if both \(y_{i}^{1}\) and \(y_{i}^{2}\) are greater than or equal to \(CT_{t}\).
# A Review of Traffic Flow Prediction Methods in Intelligent Transportation System Construction
## 2 Prediction Method Based on Statistical Analysis Theory  
### Introduction to Traditional Statistical Methods  
Traditional statistical methods have been in use for an extended period in traffic flow prediction, particularly in the early stages of intelligent transportation system development. While deep learning and machine learning techniques have gradually gained prominence in recent years, traditional statistical methods still hold significant value in short-term traffic forecasting or scenarios with limited data. These methods are widely adopted for short-term traffic prediction due to their relatively simple theoretical foundations, lower computational complexity, and stable prediction results. The essence of traditional statistical methods lies in constructing mathematical models based on historical traffic data to capture characteristic patterns from time series, thereby enabling the prediction of future traffic flows.