esa-sceva/satcom-chunk-collection huggingface dataset:
# Variable Admittance Control in Sliding Mode for Robust Physical Human-Robot Interaction
## 2 Human-Intention-Based VAC and PER
### Power Envelope Regulation  
Power envelope regulation presented in this section aims to regulate the admittance parameters inferred from human intentions from a perspective of safe interaction and system passivity. The power envelope bounds the injected power to the human-robot coupled system.  
Equation (2) allows easier leading by reducing damping when a higher force is applied, representing the human intention of accelerating. Nonetheless, smaller damping renders a less dissipative system. If human-injected power is too high, stability or safety may be compromised. The system's input power must be regulated by a prescribed power envelope profile to ensure safety. To this end, a soft power envelope is defined as follows:  
\[P_{soft}^{i}=f_{n}^{i}\dot{x}_{max}^{i}\ i=1,2,\ldots,n \tag{4}\]  
where \(\dot{x}_{max}^{i}\) denotes the maximum safe speed of operation in each dimension. If the interaction force still yields an increasing velocity greater than the maximum safe velocity, the system should increase the damping. Correspondingly, a new updating rule for variable damping will be adopted when the injected power exceeds the soft power envelope, which is given as  
\[\mathbf{D_{d}}(t)=diag\bigg{(}\mathbf{f_{h}}(t)\cdot\dot{\mathbf{x}}_{max}\bigg{)} \tag{5}\]  
Stability is an overarching safety condition in pHRI and takes precedence over tracking performance. Therefore, constraints on stiffness should also be imposed by including the passivity condition. Though satisfying the passivity condition does not guarantee stability, it suppresses stiffness from increasing excessively. [PERSON] et al. suggested that increasing stiffness injects extra power into the system [31]. Therefore, to maintain the passivity as an additional safety measure against instability, the following constraint is added to the variable stiffness:  
\[\frac{1}{2}\dot{K}_{d}^{i}\ (t)\dot{\widetilde{x}}_{i}^{2}\leq\mu D_{d}^{i} \ \dot{\widetilde{x}}_{i}^{2},\ i=1,2,\ldots,n \tag{6}\]  
where \(0<\mu<1\). The power envelope for regulating an extra injected power due to a time-varying stiffness is set on dissipative power, i.e., \(D_{d}^{i}\ \dot{\widetilde{x}}_{i}^{2}\).
# Evaluation of Five Reanalysis Products over France: Implications for Agro-Climatic Studies
## 3 Results
### Evaluating the RPs for Apple Crop Indicators  
For DB indicator, strong correlations between all RPs and the reference dataset are observed (LCCC values \(\geqslant\) 0.9, Table 3). For most stations, the LCCC values between the RPs and the reference dataset are high. The ratio of stations with LCCC values greater than 0.9 is 30%, 70%, 80%, 80%, and 100% for SCOPE Climate, ERA5, FYRE Climate, ERA5-Land, and, RFHR respectively (Figure 6d). Regarding the PBias, RPs exhibit, on average across all sites, values ranging between \(-\)3% and 15% (Table 3). Compared to RFHR, the KDE curves of the other RPs show a large spread, suggesting a higher variability (Figure 6b). For example, ERA5 overestimates the DB date by more than 370%. (Figure 6c) shows good agreement between the cumulative probability distributions of RPs and the reference dataset. The IQD values also suggest a similarity between the RPs and the reference dataset, with values below 0.3 in most cases (considering all stations and RPs) (Figure 6f).  
While RPs lead to satisfactory and similar results for most of the stations, the Embrun site presents an exception. All RPs perform rather poorly for this station except for RFHR, which returns satisfactory results (LCCC = 0.9, PBias = 3.77, and IQD = 0.07).  
Figure 5: Same as Figure 2, with the indicator DDs.  
As illustrated in Table 3 and Figure 7d, FYRE Climate displays the best agreement with the reference dataset for the WR indicator. Despite the lower LCCCs, the agreement between ERA5, ERA5-Land, RFHR, and the reference dataset can be considered as acceptable. In contrast, SCOPE Climate consistently shows a lower agreement with the observations (on a global scale and for 80% of stations), with LCCCs below 0.6. Regarding the IQD, FYRE Climate gives the lowest values (Table 3 and Figure 7c), indicating a closer similarity to the reference dataset compared to the other RPs. ERA5 and ERA5-Land have relatively close values, indicating comparable performance. Despite the lower IQD value, RFHR still displays higher values compared to ERA5 and ERA5-Land for 60% of the stations (Figure 7f). This discrepancy between RFHR and the reference dataset is particularly notable at the Brest-Guipavas station, where the IQD value is three times higher compared to ERA5 and ERA5-Land. In addition, all RPs exhibit positive biases on average, suggesting a tendency towards an overestimation of WR (Table 3). Except for the Brest-Guipavas station, for which all the RPs show an underestimation of WR compared to the reference dataset, the overestimation of WR is still noticeable in most cases (considering all the stations and RPs) (Figure 7e).  
Figure 6: Same as Figure 2, with the indicator DB.
## 3 Numerical Simulation of Drop Process of Single DOF System
### Drop Impact-Rubbings Behavior of Bilateral Failure Models  
#### 3.3.1 Influence Law of Structural Parameters on Rotor Impact-Rubbing Behavior  
In bilateral failure model, the maximum radial impact force of rotor/stator collision varies with the structural parameters as shown in Figure 9.  
According to Figure 9, maximum impact force \(F_{m\max}\) changes very little within the range of selected structural parameters, ranging from 72.5 to 72.6. It will be invariable with the change of plating thickness, but increases with the increase of liquid film thickness. The maximum value of \(F_{m\max}\) occurs in the combination of \(h_{0}\) = 80 \(\upmu\)m and \(l\) = 50 \(\upmu\)m. Therefore, take it as a typical example in order to analyzing operation rules as shown in Figure 10.  
According to Figure 10, in initial equilibrium stage, rotor dropped from zero position and stays at \(-39.3~{}\upmu\)m after a small vibration. The load is borne by both electromagnetic force and hydrostatic force as shown in Stage 1.  
In dropping Stage, rotor drops rapidly. The current \(i_{My,1}\) of supporting unit 1 and the current \(i_{My,2}\) decreases to 0, and electromagnetic force also becomes 0. The liquid resistance \(R_{h1}\) has little change, while the liquid resistance \(R_{h2}\) rises rapidly due to the impact-rubbing damping effect of oil. Therefore, the hydrostatic force increases rapidly first and then decreases slowly as shown in Stage 2.  
In collision Stage, rotor reaches a new equilibrium state. As the contact depth between rotor and stator gradually increases, the impact force \(F_{n}\) increases, and the load of the rotor is borne by electromagnetic force \(F_{My}\) and hydrostatic force \(F_{Ly}\). When electromagnetic system fails completely, and electromagnetic force \(F_{My}\) becomes 0. The liquid resistance \(R_{h1}\) and \(R_{h2}\) are respectively stable at \(5.0\times 10^{8}\) N\(\cdot\)s/m and \(9.0\times 10^{12}\) N\(\cdot\)s/m. The latter is  
Figure 9: Maximum radial collision force bilateral failure model.  
about \(1.8\times 10^{4}\) times of former, resulting in a tiny tremor after a collision between rotor and stator. Moreover, hydrostatic force \(F_{My}\) decreases rapidly and finally stabilizes at about 67.4 N as shown in Stage 3.  
Compared with upper unit failure, the variation of liquid film thickness and plating thickness does not affect the degree of rotor/stator collision. Dropping time is shorter, the stator/rotor contact arrives faster after collision Stage, the biggest impact force averages 72.6 N, less than upper unit failure model. Thus \"fall-collision\" effect on the bearing system is smaller under this model.
# OPMSS: Optimal Producer Mobility Support Solution for Named Data Networking
## 2 Related Works  
To achieve the routing and forwarding of packets, NDN uses named-base routing and possesses of two different types of packets, namely interest and data packets. Additionally, the NDN node can be represented as client consumer, producer or a router, which maintains three aspects--data structure pending interest table (PIT), forwarding information base (FIB) and content store (CS)--and determines when and where to forward data and interest [1; 31]. For the purpose of PIT records and storing any incoming interest information, FIB maintained the forwarding strategy and decided when and where to forward interest and CS; this is a temporary cache of data stored based on NDN caching policy. The network was set up and the client node (consumer) was established as a connection in the network. When the consumer intends to retrieve data from the network, it will send an interest to the NDN router. On arrival of the interest, the router checks the CS for matching data; if found, it forwards the data packets back to the consumer. Otherwise, the router looks up the name content in its PIT for matching entries--if the entry is not found, it will record the name content and incoming interface and forward the interest to the next hop through FIB, otherwise it will aggregate and record the interface only. The same process takes place up towards the content producer. Interest can be dropped on certain circumstances depending on the forwarding strategy, e.g., the congestion of upstream links or security breaches by suspecting interest to be part of DoS. Once the data producer receives an interest request from the home router and its PIT, and finds the required content, it will forward the data packets back through the interface that received the interest to the downstream interface recorded in PIT [32; 33].
# Multi-Task Learning for Small Brain Tumor Segmentation from MRI
## 2 Related Work
[51] introduced a methodology to integrate human brain connectomics and parcellation for brain tumor segmentation and survival prediction. [PERSON] et al. [52] designed a new separable 3D convolution by dividing each 3D convolution into three branches in a parallel fashion, each with a different orthogonal view, namely axial, sagittal and coronal. They also proposed a separable 3D block that takes advantage of the state-of-the art residual inception architecture. However, these networks are not for the hierarchical analysis nor targeted for small tumors. Multi-task learning has been successful in many applications of deep learning including the brain tumor segmentation. [PERSON] et al. [53] exploited multi-task learning for brain tumor segmentation with an auxiliary task of classification. Their network proved the effectiveness of the multi-task learning on the brain tumor segmentation. [PERSON] [54] proposed a U-Net based model with an additional variational autoencoder branch for reconstructing the input images. The image reconstruction task is jointly trained with the segmentation task. Although this architecture worked well for segmentation in general, their model requires a large memory and too heavy computation cost. U-Net is a very successful network in biomedical image segmentation. It is the first choice when dealing with medical imaging segmentation. However, U-Net gradually reduces the feature resolution through down-sampling layers. This process leads to the poor-segmentation on small-sized objects. There are several methods that have been proposed to address small object problems. [PERSON] et al. [55] applied a patch-based and pixel-to-pixel based method for improving the segmentation performance on small objects in urban remote sensing images. [PERSON] et al. [8] proposed a small tumor-aware network with multiple kernel sizes for accurate segmentation of small tumors in breast ultrasound images. These methods work well on 2D images but may not operate efficiently on MR images, where the data is in the 3D form. In this paper, we propose a network that focuses on small-sized tumors in the brain tumor segmentation problem. Our network uses multi-task learning for retaining important features of small tumors, which is often lost during the downsampling process of original U-Net. The experimental results show that the auxiliary task in our model is helpful for the small tumor segmentation task.
# Low-Cost Data-Driven Robot Collision Localization Using a Sparse Modular Point Matrix
## 2 Materials and Methods
### Collision Localization Model
#### 2.3.1 CNN  
The CNN, a representative deep learning method, is known for its efficacy in processing time-series and image signals [16; 17]. A typical CNN structure comprises two main components: the convolutional layer and the pooling layer. In the convolutional layer, the convolution operation is applied between the input features and convolution kernels, resulting in the generation of new features. Following convolution, the obtained results typically undergo non-linear processing, often facilitated by activation functions. Commonly employed activation functions include Sigmoid, tanh, and ReLU [18]. Based  
Figure 2: Different sparsification forms of SMPMs.  
on previous research results, ReLU activation functions are a suitable choice for collision localization problems.  
The pooling layer serves two primary functions: dimensionality reduction and mitigating overfitting. There are two main types of pooling methods: average pooling and maximum pooling. In average pooling, the operation involves taking the average of the convolution-derived features as the output, while in maximum pooling, the operation involves selecting the maximum value from the convolution-derived features as the output. In this research, we adopted the same pooling method as in our previous work, specifically utilizing the maximum pooling approach.
# Analytical Solutions of Water Inflow for Foundation Pit in Confined Water Stratum
## 4 Verification and Analysis  
### Supplementary Cases Verification  
The water inflow of the circular foundation pit under different cases is chosen for verification and analysis. Figure 6a shows the comparison between the analytical solution and numerical solution calculation results of water inflow under verification cases. From Figure 6a, it can be seen that the water inflow decreases nonlinearly as the \(L/M\) increases and the analytical solutions are consistent with the numerical simulations. The maximum RMSE of the two results is 4.18. Figure 6b shows the comparison between the two results of \(H_{w}\) under the cases of \(r_{0}/M=4\), 6, and 10. It can be seen from the figure that the two results have the same trend and \(H_{w}\) increases nonlinearly as the \(L/M\) increases. When \(L/M>0.7\), the \(H_{w}\) obtained by the two methods is basically the same. When \(0<L/M<0.7\), the analytical solutions are slightly smaller than the numerical solutions. The maximum RMSE of the two results is 3.32.  
In order to verify the universality of the derived solutions, several parameters include the soil permeability coefficient \(k\), confined aquifer thickness \(M\), and the difference in hydraulic head \(H\)-\(H_{d}\) are changed, and the water inflow under changed parameters are calculated. Figure 7 shows the water inflow of the analytical solutions and numerical results under changed parameters. The figure shows that the water inflow decreases nonlinearly as the \(L/M\) increases under all changed parameters. The water inflow increases with the increase in the soil permeability coefficient \(k\), confined aquifer thickness M, and the difference in hydraulic head \(H\)-\(H_{d}\), respectively.  
Figure 5: The variation in water inflow with the change of \(L/M\) for different shapes of foundation pits.  
Figure 6: Water inflow and \(H_{w}\) in different cases of foundation pit.  
Figure 7: \(Cont.\)
# Satellite Image Compression Guided by Regions of Interest
## 2 Materials and Methods
### Scenarios  
Experiments were conducted considering typical scenarios containing clouds, vessels, and small changes on dry land. One area in Stockholm, Sweden, composed of both dry land and water regions, was arbitrarily selected--around the Skarpo region. The area has the following approximate central coordinates: 18.75991, 59.33307 for WGS84; and 372579, 6579199 for EPSG 32634. Then, some images used by a Saab-developed AI-based data fusion system were selected. The images were from PlanetScope, which is a constellation of approximately 130 satellites (orbit type LEO-SSO) that is able to image the entire land surface of the Earth every day [25]. The images have approximately 3m per pixel resolution and 16 bpp precision (bit depth); they are composed of 4 bands (PSScene4 Band): blue, green, red, and near-infrared bands. Altogether, 25 images were selected and cropped to the selected area of 1300 per 1300 pixels. More precisely, the 25 images were randomly selected from those covering the selected area within the time range of approximately 8:44 to 10:10 (considering summer and winter days). The time range was limited to these values to select images with similar solar conditions, which allowed the use of a fixed threshold value for the detection algorithms during the experiments. Table 1 gives more details about the selected images. The labels listed in the last column of Table 1 were defined after a careful, deep visual examination of the images. Then, the images were grouped as no-clouds, cloudy, small clouds, fog, snow and clouds, and snow and no-clouds. The selected images have possible vessels in the water region, excluding images 1 and 21-24, in which highlighted pixels were not detected in the water region. It is worth mentioning that the dataset is not labeled. Thus, some possible vessels could be the results of noise, movements in the water due to the wind, or very small vessels (e.g., surfboards and jet-skies). However, as the objective was to mark ROI containing possible vessels, lack of labeling was not an issue. In other words, possible vessels can be losslessly compressed in addition to real vessels to avoid loss of interesting content for the application. Figure 6 shows two images of the selected area as examples. The maximum amplitude of the pixels was truncated at 2000 to simplify the visualization of the images in this paper (i.e., the pixel values higher than 2000 are printed as being equal to 2000).
## 3 Tests' Description  
### General Description  
The tests were carried out on 12 RC beams (300 \(\times\) 120 \(\times\) 1500 mm, Figure 1), designed according to Chinese standards GB/T 50081-2016 [44]. The different stages of the experimental tests are presented in the form of a flowchart in Figure 2 (for more details see [4]). Stage I includes the fabrication and curing (28 days) of the reinforced concrete specimens. RC specimens have made from commercial concrete with a target strength of 40 MPa at 28 days and a water/cement ratio 0.44 chosen according requirements of [45] for the concrete elements in a chloride-containing environment. The composition and additional details of concrete are provided in Table 1.  
Figure 1: Reinforced-concrete test specimen.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{2}{c}{**Target Concrete Strength**} & **C40 MPa** \\ \hline \multirow{7}{*}{Materials and Mix Proportion} & Water (tape water) & 180 kg/m\({}^{3}\) \\ \cline{2-3}  & Cement (PII 52.5) & 320 kg/m\({}^{3}\) \\ \cline{2-3}  & \multicolumn{2}{c}{Furnace micro-slag (S95)} & 90 kg/m\({}^{3}\) \\ \cline{2-3}  & Aggregates & Fine aggregates (middle size) & 770 kg/m\({}^{3}\) \\ \cline{2-3}  & \multicolumn{2}{c}{Coarse aggregates (5–25 mm, crushed)} & 1020 kg/m\({}^{3}\) \\ \cline{2-3}  & \multicolumn{2}{c}{Super plasticizer (LN800)} & 4.51 kg/m\({}^{3}\) \\ \hline \multirow{2}{*}{Properties} & Slump (mm) & 140 \(\pm\) 20 mm \\ \cline{2-3}  & 28-day compressive strength 150 \(\times\) 150 \(\times\) 150 mm concrete cube (MPa) & 50.0 MPa \\ \hline \hline \end{tabular}
\end{table}
Table 1: Characteristics of the concrete mixture [4].  
Figure 2: Stages of experimental tests.  
In stage II (Figure 2), the specimens were subjected to no loading (4 beams), static loads (single load of 18 kN, 4 beams) and cyclic loads (500,000 loading cycles 5.4k N to 18 kN, 4 beams) using a three-point loading test. The control and record of static and cyclic load magnitudes were carried out by a load cell [46].  
After the mechanical tests, the beams were exposed to chloride ions by wetting and immersing the beams in a 3.5%-5% NaCl solution (Stage III in Figure 2). The beams were first covered with a sponge wetted with 3.5%-5% NaCl solution and dried. Afterwards, they were completely immersed in a pool filled with this same solution and dried. A total of four wetting/drying cycles and three immersion/drying cycles were performed during 388 days [47].  
Once the chloride exposures were completed, four cylinders (100 mm diameter \(\times\) 120 mm height) were drilled in the center zone of each beam (Stage IV in Figure 2). In Stage V, the cylinders were cut in 10 mm slices. These slices were oven dried and ground to powder to make a complete blend [4]. The resulting powders were used to measure the total content of concrete chlorides at four depths (5 mm, 15 mm, 25 mm and 35 mm). Three categories of tests were studied according to the loading conditions: uncharged tests, static and cyclic loading. In total, 12 chloride profiles were measured for each category.
# Security Analysis of the Symmetric Cryptosystem Tiny Jambu
## 3 Security of TinyJambu against Cryptanalytic Attacks
### The Concept of Active AND Gates
In TinyJambu, this kind of attack is launched in the initialization phase (either in the nonce or associated data setup) as both initializations make use of the keyed permutation \(P_{l}\) with \(l=384\), which is the minimum number of rounds in the cryptosystem design. As long as the probability \(p\) satisfies the inequality \(p\geq 2^{-64}\) (see [7], Section 4.2), a differential attack breaks the 64-bit security claimed by the designers in [6].