esa-sceva/satcom-chunk-collection huggingface dataset:
# Al-Foam Compression Tests in Parallel and Serial Concepts
## 8 Specific Energy Absorbing (SEA)  
When weight is a crucial project parameter, Specific Energy Absorbing (SEA) enables the comparison of the energy absorption capacities of the two different materials and designs. It goes without saying that the energy absorption capacity of a crumple structure can be increased by changing the density of the foam used [28; 29; 30]. However, this is limited by mass optimization. Therefore, SEA is an excellent value for determining how much energy a unit mass of foam or foams can absorb.  
\[\text{EA}=\int_{0}^{\delta}\text{F}(\text{x})\text{d}\text{x} \tag{5}\]  
\[\text{SEA}_{\text{mass}}=\frac{\text{EA}}{\text{m}} \tag{6}\]  
\[\text{SEA}_{\text{volume}}=\frac{\text{EA}}{\text{V}} \tag{7}\]  
where the m is the mass, the V is the volume of the specimens, and the EA is the amount of the absorbed energy, given in J. Basically, it is a value given by the ratio of the absorbed energy to the volume or mass. This can certainly be a guideline for the development of a mass optimization passive safety system. With SEA, the performance of the development can be expressed objectively, and it may also help to find the most effective design for passive safety systems [31; 32; 33; 34]. Before the compression tests, the volume and mass of the foams were recorded and can now be used to determine the SEA in J/g. According to the content in Table 6, it was revealed that the different density foam had a positive effect on the SEA. Furthermore, applying the same foam, this value could even be further influenced by the positioning of the foams either in parallel or in series. In the case of crumple zone development overweight optimization, the space requirements are also important conditions. Hence, this study investigated the SEA value relative to the volume of the foams. This is also a great way to investigate the effect of foam placement, as both the parallel and in-line arrangements used the same volume unit, but the amount of energy absorbed, as shown in the previous table, was not the same, resulting in different overall efficiencies. Analyzing the data in Table 6 reveals that the effect of the different scenarios is well established. In the case of SEA, with regards to volume, there is no remarkable deviation between each concept, except in the serial method. With the serial design, the characteristic of the curve may be influenced, rather than the amount of energy.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Type of Method** & **Labels** & **\(\text{\text{\text{\text{\text{\text{\text{\text{\
# Speaker Diarization: A Review of Objectives and Methods
## 3 Analysis of Speech Signals
### Intonation Features  
In speech, patterns of timing and F0 are formed by speakers to assist listeners to make linguistic decisions [25]. Nonetheless, very few speech classification applications (including SD) exploit intonation, as the range and diversity of intonation are difficult to integrate into frame-based classifiers.  
For phone or word durations, automatic estimation is difficult, as ASR methods may not be reliable. Many algorithms exist to estimate F0 [29], but intonational patterns require a wide range of speech frames, whereas VT resonances are relevant over short periods.  
Nonetheless, intonation has been used for SD in the past [30; 31; 32]. The range and average value of F0 in speech are both speaker-specific, and F0 contours are generally slowly changing, which help with both speaker identity and continuity. In general, the features (both for resonances and intonation) chosen for use in SD have been statistical (e.g., means, ranges, and standard deviations), rather than dynamic (e.g., F0 contours over syllables). It may be insufficient for SD to ignore more global, dynamic feature behaviour, which humans use a lot, simply because it is inconvenient to integrate into modern SD tools. Much of what is known about human speech production and perception has been ignored in modern neural systems for speech applications, because this information has been difficult to exploit in the traditional frame-based approaches.
# Review of the AI-Based Analysis of Abdominal Organs from Routine CT Scans
## 2 Historical Evolution
### Deep Learning Revolution (2018-2024)
#### 2.3.8 Deep Learning Algorithm (DLA)  
In 2020, [PERSON] et al. developed a method to address time-consuming segmentation for liver and spleen volumes. They used a DLA for fully automated segmentation in portal venous phase CT images across various liver conditions. The DLA was trained on a dataset comprising portal venous phase images from 813 patients and evaluated across two separate test datasets: the first included 150 CT examinations of patients with conditions ranging from healthy to cirrhotic livers and post-Hepactotomy cases; the second dataset comprised 50 pairs of CT examinations from different institutions. The DLA demonstrated high accuracy with mean Dice similarity score (DSS) of 0.973 and 0.974 for liver and spleen,respectively, across varying liver conditions and showed no significant performance drop in images from external sources, highlighting its robustness and potential for clinical application [19].
## 7 Discussions and Clarification of Comparison Results
### Secondary Configurations (i.e., LTP UDP SEC and LTP TCP SEC)
#### 7.3.1 UDP on the Proximity Segment (i.e., LTP UDP SEC)  
In this configuration (as discussed for UDP LTP PRIM in subsection B-2), significant congestion losses happen in the transmission of data from the Earth orbiter to the surface of Earth by UDP (without any recovery from data loss, as would be provided by LTP). This means that the rate of data arrival at the surface is always lower than the rate at which the cislunar segment delivers data to the orbiter. That accounts for the manner in which the goodput curve for LTP LTP PRIM is roughly the same as the curve for LTP LTP PRIM, but with a negative offset. The effective rate of arrival of data at the orbiter is initially low and decreases with increasing signal propagation latency on the cislunar segment, so the opportunity for congestion loss on the proximity link is minimized, but any congestion loss that does occur is uncorrected and further degrades goodput. This is the worst-case scenario.  
7.3.2 TCP on the Proximity Segment (i.e., LTP TCP SEC)  
In this configuration, the use of TCP for the transmission of data from the Earth orbiter to the surface of Earth precludes data loss on the proximity segment. Again, the cislunar segment's congestion loss and recovery \"screens\" most potential congestion loss out of the proximity segment. Unlike in the configuration of LTP UDP SEC, any congestion loss that does occur on the proximity segment is immediately corrected by TCP, so the performance of LTP TCP SEC will always be somewhat better than that of LTP UDP SEC. However, as congestion loss increases due to increasing signal propagation delay on the cislunar segment, the rate of data arrival at the orbiter drops and the opportunity for congestion loss on the proximity segment decreases, reducing the relative advantage of TCP over UDP on that segment.
## 6 Conclusions  
In this paper, PM-Net was proposed to solve optical and SAR image patch matching problems involving MKD and patch feature learning. First, MKD combined multi-level feature maps to obtain richer information from images for keypoints detection. Second, t wo-channel network was presented for learning the mapping function from image patch to matching label directly. This completes image matching by judging the matching labels of image patch pairs. Through comparison experiments with Contextdesc, R2D2, D2-Net and ASLeFeat, PM-Net had obvious advantages in terms of NCM, F1-measure and RMSE in six  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Metric**} & \multirow{2}{*}{**Test Number**} & \multirow{2}{*}{**SIFT+MM**} & \multirow{2}{*}{**D2-Net + MM**} & **MKD (w/o Peak** & **MKD (w/o Weighted** & \multirow{2}{*}{**MKD + MM**} \\  & & & **Values) + MM** & **Combination) + MM** & **MKD + MM** \\ \hline \multirow{8}{*}{\%Rep.} & Test 1 & 44.18 & **62.70** & 26.00 & 30.00 & 61.06 \\  & Test 2 & 49.56 & 47.27 & 28.24 & 42.86 & **53.33** \\  & Test 3 & 21.13 & 31.46 & 45.28 & 33.33 & **48.05** \\  & Test 4 & 41.57 & 44.72 & 26.09 & 39.13 & **55.07** \\  & Test 5 & 38.51 & **63.56** & 35.91 & 39.89 & 59.87 \\  & Average (1–10) & 38.70 & 46.64 & 30.91 & 38.18 & **53.19** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Ablation experiment result of keypoints detection.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Metric** & **Test Number** & **MKD + PSN** & **MKD + SN** & **MKD + 2 chN** \\ \hline \multirow{8}{*}{\%Precision} & Test 1 & 38.72 & 40.89 & **57.05** \\  & Test 2 & 31.93 & 61.69 & **66.36** \\ \cline{1-1}  & Test 3 & 36.84 & **51.49** & 45.38 \\ \cline{1-1}  & Test 4 & 34.13 & 22.98 & **38.89** \\ \cline{1-1}  & Test 5 & 41.19 & 35.64 & **49.87** \\ \cline{1-1}  & Average (1–10) & 38.01 & 40.69 & **48.28** \\ \hline \hline \end{tabular}
\end{table}
Table 5: Ablation experiment result of patch matching.  
pairs of optical and SAR images. Furthermore, the structural advantages of PM-Net were verified in ablation experiments with ten pairs of test data. However, the time consumption of PM-Net is large due to the brute-force matching strategy; to improve the time efficiency of it by parallel processing will be our future work.  
Conceptualization, Z.L.; methodology, Z.F.; writing--review and editing, H.N.; data curation, S.C.; All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the National Natural Science Foundation of China under grant nos. 41961053. It was also supported by Yunnan Fundamental Research Projects under grant nos.202101 AT070102 and 202101 BE070001-037.  
The main code and all data in the paper are open source on [[https://github.com/Liziqian-1012/PM-net](https://github.com/Liziqian-1012/PM-net)]([https://github.com/Liziqian-1012/PM-net](https://github.com/Liziqian-1012/PM-net)) (accessed on 10 June 2022).  
The authors declare no conflict of interest.
# Experimental Investigation on Shear Behavior of Dune Sand Reinforced Concrete Deep Beams
## 4 Discussion
### Effect of Shear Span-to-Depth Ratio  
Figure 8a shows the relationship between shear stress and shear span-to-depth ratio for DSRC deep beams and NRC deep beams. All NRC deep beam specimens in Figure 8a are shown with shear span-to-depth ratio as the variable, including: 32 sets from [PERSON] et al. [46], 16 sets from [PERSON] et al. [47], 13 sets from [PERSON] et al. [48], 12 sets from [PERSON] and [PERSON] [49] and 17 sets from [PERSON] et al. [50]. For DSRC beams, the variation in shear strength with shear span-to-depth ratio was similar to that for NRC beams. The ultimate shear stress decreased as the shear span-to-depth ratio increased. The shear strength of specimens B1 and B2 decreased by 21.8% and 27.4%, respectively, when compared to the specimen B2 (ultimate shear stress \(v_{\text{n}}\) = 6.85 MPa). This decrease occurred because as the shear span-to-depth ratio of the deep beam increased, the angle between the strut and tie increased, the arch effect weakened, the shear mechanism gradually transitioned from arch action dominant to beam action dominant, and the role of concrete struts to improve the shear strength gradually disappeared.
# Mechanistic Insights into Fish Spoilage and Integrated Preservation Technologies
## 3 Preservation Technologies and Optimization
### Freezing
#### 3.1.2 High-Pressure Treatment (HPP)  
High-pressure treatments are effective in preserving fish by causing lethal structural and biochemical alterations in micro-organisms, similar to high-temperature treatments. HPP vessels are cylindrical, water-filled pressure chambers constructed of thick, high-strength steel with internal diameters ranging from 200 to 500 mm; axial pumps deliver up to 600 MPa isostatic pressure, while internal temperature is maintained at 4-10 \({}^{\circ}\)C via heat exchangers to prevent thermal spikes [64]. Micro-organisms are inactivated under high pressure due to cell deformation and damage. However, these effects are reversible at pressures between 100 and 300 MPa and are insufficient for inactivating resistant bacterial spores, which require pressures as high as 1200 MPa [65].  
High-pressure treatment at 200 MPa combined with sub-zero temperature has been shown to inhibit Listeria monocytogenes in smoked salmon, although it can also lighten color and toughen texture [21]. In vacuum-packed trout, high-pressure treatment extended shelf life from 5-6 days to 21-28 days by reducing biogenic amine content [66]. Conversely, pressure treatments caused bright and dull red appearance in species such as turbot, sheep head, cod, and carp [67; 68; 69].  
A study on rainbow trout and mahi found that high-pressure processing at 300 MPa and 450 MPa, respectively, was optimal for controlling microbial load, lipid oxidation, and color changes [70].
## 2 The Optimization Procedure
### The [PERSON]'s Weld Description  
In order to perform the ultrasonic imaging, one needs a weld description that preserves a realistic propagation of QL waves. In the optimization procedure, the [PERSON]'s weld model has been chosen for this task [20]. This description only requires a few parameters to provide an analytical law that defines the varying grain orientation. It is an instantaneous description of the weld and only adds four degrees of freedom to the optimization problem, when the model is applied in its simplest form for a symmetrical weld. This description is preferred over other approaches that would be more time consuming and add more parameters to the problem [15; 16; 21; 22].  
The grain orientation \(\theta\) in the weld is defined by [PERSON]'s model as:\[\theta=\left\{\begin{array}{cc}\arctan\left(\dfrac{T_{1}(D_{1}+z\tan\alpha_{1})}{ x^{\eta_{1}}}\right)&\text{ with }x>0\\ -\arctan\left(\dfrac{T_{2}|D_{2}+z\tan\alpha_{2}|}{|x|^{\eta_{2}}}\right)&\text{ with }x<0\\ \dfrac{\pi}{2}&\text{with }x=0\end{array}\right.. \tag{1}\]  
The parameters to build this model are illustrated and summarized in Figure 1.  
In the following numerical proof of concept, we use the simplest form of the [PERSON]'s model where \(D_{1}=-D_{2}\), \(\alpha_{1}=-\alpha_{2}\), \(T_{1}=-T_{2}\) and \(\eta_{1}=\eta_{2}\), and the weld crystalline structure is assumed to be orthotropic. The optimization procedure then involves 9 degrees of freedom: four [PERSON]'s model parameters, four components of the elastic tensor to account for the 2D QL waves propagation (\(C_{11}\), \(C_{13}\), \(C_{33}\), \(C_{55}\)), and \(\theta_{y}\) the local disorientation of the polycrystalline structure with respect to \(\theta\) in the imaging plan. This parameter indicates that the privileged dendritic growth during the weld cooling forms a \(\theta_{y}\) angle with the main direction of the polycrystalline structure.
_Applied_  
_sciences_  
_Article_  
**Analysis of the Use of Geomorphic Elements**  
**Mapping to Characterize Subaqueous Bedforms**  
**Using Multibeam Bathymetric Data in River System**  
**[PERSON] 1\(\copyright\)**, **[PERSON] 1,2,*, [PERSON] 1, [PERSON] 1, [PERSON] 3, [PERSON] 3 and [PERSON] 3**  
1 State Key Laboratory of estuarine and Coastal Research, East China Normal University, Shanghai 200000, China; [EMAIL_ADDRESS] (G.Y.); [EMAIL_ADDRESS] (L.T.); [EMAIL_ADDRESS] (W.X.)  
2 Institute of Eco-Chongming, Shanghai 200000, China  
3 Nanjing Geological Survey Center, China Geological Survey, Nanjing 210000, China; [EMAIL_ADDRESS] (Y.J.); [EMAIL_ADDRESS] (G.Y.); [EMAIL_ADDRESS] (Q.Z.)  
* Correspondence: [EMAIL_ADDRESS]; Tel.: +86-21-54836006 check for updates  
Received: 2 September 2020; Accepted: 26 October 2020; Published: 30 October 2020  
**Featurered Application: This work has application in separating subaqueous dune areas from the flat bed and assessing the scouring risk associated with the pit distribution. It verifies the feasibility of applying landscape analysis methods to characterize subaqueous micro-topography.**  
**Abstract:** Riverbed micro-topographical features, such as crest and trough, flat bed, and scour pit, indicate the evolution of fluvial geomorphology, and have an influence on the stability of underwater structures and overall scour pits. Previous studies on bedform feature extraction have focused mainly on the rhythmic bed surface morphology and have extracted crest and trough, while flat bed and scour pit have been ignored. In this study, to extend the feature description of riverbeds, geomorphic elements mapping was used by employing three geomorphic element classification methods: [PERSON]'s criteria, a self-organization map (SOM) technique, and geomorphons. The results showed that geomorphic element mapping can be controlled by adjusting the slope tolerance and curvature tolerance of [PERSON]'s criteria, using the map unit number and combination of the SOM technique and the flatness of geomorphons. Relatively flat bed can be presented using \"plane\", \"flat planar\", and \"flat\" elements, while scour pit can be presented using a \"pit\" element. A comparison of the difference between parameter settings for landforms and bedforms showed that SOM using 8 or 10 map units is applicable for land and underwater surface and is thus preferentially recommended for use. Furthermore, the use of geomorphons is recommended as the optimal method for characterizing bedform features because it provides a simple element map in the absence of area loss.  
**Keywords:** multibeam; flat bed; scour pit; Wood's criteria; self-organization map; geomorphons
# Off-Grid Power Plant Load Management System Applied in a Rural Area of Africa
## 6 Experimental Results and Comparison
### Comparison and Evaluation
#### 6.2.2 Comparison of Anomaly Detection Results  
The anomaly detection results are compared to discuss the effect of introducing the FOA to improve the classification performance of the SVM and the reasonability of using a variable detection threshold in the detection process. The detection results of the general SVM model with a fixed penalty factor and FOA-SVM with an optimized penalty factor are reported in Table 3.  
The measure introduced is the \(F_{1}\) score, which provides a balanced evaluation of the overall performance of a classifier. The \(F_{1}\) score consists of precision \(p\) and recall \(r\), which were developed by the information retrieval community. \(p\), \(r\), and the \(F_{1}\) score are defined as follows [17]:  
\[\begin{array}{l}p\ =\ \frac{TP}{FP+TP}\\ r\ =\ \frac{TP}{FN+TP}\\ F_{1}\ =\ \frac{2 TP}{p+r}\end{array} \tag{10}\]  
where \(TP\) represents the number of time points at which normal data were correctly classified, \(FP\) represents the number of time points at which anomalous data were classified as normal, \(TN\) represents the number of time points at which anomalous data were considered abnormal, and \(FN\) represents the number of time points at which normal data were labeled abnormal [17].  
The results show that FOA-SVM with variable parameters outperforms the general SVM with fixed parameters. In particular, according to the characteristics of residential electrical power usage, using variable parameters to increase the detection accuracy not only improves the performance of anomaly detection but also provides a rational detection approach that can be used as a reference for future research.
## 5 TAFLRLR Protocol
### DATA Receiving  
After Node N sends the DATA packet, the neighbor nodes within the transmission range of the sending node N can hear the DATA packet. The receiving flow of the DATA packet is shown in Figure 12, where Self ID represents the ID of the current receiving node and the Rece ID represents the ID of the destinated receiving node assigned by the head field of the packet. According to the Rece_ID, there are three cases of data receiving [17].  
Figure 11: The forwarding flow of the TAFLRLR protocol.  
Figure 12: The receiving flow of the TAFLRLR protocol.  
Case 1: If the node that hears the packet is a sink node, it updates the neighbor information table and receives the packet.  
Case 2: If the ID of the receiving node is the same as the \(\text{Reee\_ID}\) in the head of the packet, the receiving node updates the neighbor information table, receives the packet, and then finds the best next-hop forwarding node, updates the \(\text{Reee\_ID}\) field of the packet with the ID of the best forwarding node and sends the packet to the best forwarding node.  
Case 3: If the receiving node is neither the sink node nor the best forwarding node, it just updates the neighbor information table and discards the received packet.
## 2 Method
### Obstacle Prediction Using Fusion Prediction
#### 2.1.2 Pattern-Based Prediction: KMP  
Despite the simplicity and effectiveness of the CVM, pattern-based prediction holds a distinct advantage in its ability to learn from data. Unlike physics-based approaches that rely on predefined, parameterized functions to model motion dynamics, pattern-based methods adhere to a Sense-Learn-Predict framework. This framework enables the model to derive motion dynamics directly from data, thereby potentially enhancing prediction accuracy in complex environments.  
In our research, we utilize kernelized movement primitive (KMP) [32; 33; 34] as our pattern-based model.  
Consider \(K\) historical trajectories of length \(J\), denoted as \(\{\{\xi^{o}_{j,k^{\prime}},\xi^{o}_{j+1,k}\}_{j=1}^{J}\}_{k=1}^{K}\). Initially, we employ the Gaussian Mixture Model (GMM) [35] and Gaussian Mixture Regression (GMR) [36] to obtain a probabilistic reference trajectory \(\{\xi^{o}_{n},\hat{\vec{\mu}}_{n},\hat{\vec{\Sigma}}_{n}\}_{n=1}^{N}\) (for further details, please refer to [32]). Next, KMPs are utilized to capture and generalize the probabilistic reference trajectory. Specifically, the prediction function is expressed as follows:  
\[f^{pred}(\xi^{o}_{n})=\Theta(\xi^{o}_{n})^{T}\vec{w} \tag{4}\]  
where \(\Theta(\xi^{o}_{n})\) is a known matrix composed of basis functions, and \(\vec{w}\) is the weight vector that follows the distribution  
\[\vec{w}\sim\mathcal{N}(\vec{\mu}_{\vec{w}},\vec{\Sigma}_{\vec{w}}) \tag{5}\]  
where the mean \(\vec{\mu}_{\vec{w}}\) and the covariance \(\vec{\Sigma}_{\vec{w}}\) are unknown. The optimal solutions for \(\vec{\mu}^{*}_{\vec{w}}\) and \(\vec{\Sigma}^{*}_{\vec{w}}\) can be derived using the dual transformation of the kernel ridge regression (KRR) [37], as detailed in [32; 37; 38]:  
\[\vec{\mu}^{*}_{\vec{w}}=\vec{\Phi}(\vec{\Phi}^{T}\vec{\Phi}+\lambda_{\mu}\vec {\Sigma})^{-1}\vec{\mu}\]  
\[\vec{\Sigma}^{*}_{\vec{w}}=N(\vec{\Phi}\vec{\Sigma}^{-1}\vec{\Phi}^{T}+ \lambda_{\Sigma}\vec{I})^{-1} \tag{6}\]  
where  
\[\vec{\Phi}=[\Theta(\xi^{o}_{1})\,\Theta(\xi^{o}_{2})\,\cdots\,\Theta(\xi^{o}_{ N})]\]  
\[\vec{\Sigma}=blockdiag(\hat{\vec{\Sigma}}_{1},\hat{\vec{\Sigma}}_{2},\ldots, \hat{\vec{\Sigma}}_{N}),\]  
\[\vec{\mu}=[\vec{\mu}^{T}_{1}\,\hat{\vec{\mu}}^{T}_{2}\,\cdots\,\hat{\vec{\mu}} ^{T}_{N}]^{T}.\]  
and \(\lambda_{\mu},\lambda_{\Sigma}>0\) are regularization parameters to prevent overfitting [39].  
The kernel function \(k(\xi^{o}_{i},\xi^{o}_{j})\) allows us to avoid explicitly defining the basis function. Consequently, the kernel matrix can be computed as  
\[\vec{k}(\xi^{o}_{i},\xi^{o}_{j})=\Theta(\xi^{o}_{i})^{T}\Theta(\xi^{o}_{J})=k (\xi^{o}_{i},\xi^{o}_{j})\vec{I} \tag{8}\]  
Therefore, for a query \(\xi^{o}_{*}\), the expected value of the prediction