esa-sceva/satcom-chunk-collection huggingface dataset:
# Pathological Gait Classification Using Early and Late Fusion of Foot Pressure and Skeleton Data
## 4 Dataset Used
### The 3D Skeleton Dataset  
The most recent Kinect sensor available is the Azure Kinect. In [16], researchers utilized the Azure Kinect sensor along with the associated Microsoft software development kit (SDK) to collect skeleton data. This enabled them to capture the 3D XYZ coordinates for 32 joints, including the hips, knees, ankles, feet, nose, clavicles, shoulders, elbows, wrists, hands, hand tips, and thumbs. Figure 2 illustrates the skeleton dataset encompassing normal, antalgic, lurch, steppage, stiff-legged, and Trendelenburg gaits. The skeletal gaits of the walkers were recorded on a 4 m boardwalk. Similar to the foot-pressure dataset, gait datasets were collected for 12 subjects, comprising six different gait types and 20 trials, resulting in a total of 12 \(\times\) 6 \(\times\) 20 = 1440 skeleton data samples.
## 2 Data Source Integrity and Parametric Space Definition
### Data Source Integrity and Inclusion Criteria
Skewness values indicate slight right-skew for transport distance and energy intensity, supporting the use of non-normal distributions in surrogate model training. To ensure statistical representativeness and numerical stability during model training, each input variable was assigned a probability distribution that reflects its physical behavior and empirical spread in real-world applications. Transport Distance (X\({}_{1}\)), Moisture Content (X\({}_{3}\)), and Grid CO\({}_{2}\) Factor (X\({}_{5}\)) were modeled using uniform distributions, as they reflect a bounded yet evenly likely range within real logistic and environmental conditions. Processing Energy Intensity (X\({}_{2}\)) followed a log-uniform distribution, consistent with industrial datasets where higher values are rarer but still relevant. Gradation Adjustment (X\({}_{4}\)) was treated as a discrete ordinal variable with uniform probability across its three levels. These distribution assumptions were informed by literature-derived ranges and the need for space-filling during stratified sampling. This setup supports the learning robustness of ensemble tree models while avoiding distributional bias. In summary, the statistical architecture of the input space ensures numerical stability, dimensional coherence, and representativeness across diverse geographies and operation scales. These properties are essential for supporting the tree-based ensemble models and optimization logic described in Sections 3 and 4, and they directly impact the reliability and scope of the decision-support framework. \begin{table}
\begin{tabular}{c c c c c c c} \hline
**Variable** & **Unit** & **Min** & **Max** & **Mean** & **Std. Dev.** & **Skewness** \\ \hline Transport Distance (X\({}_{1}\)) & km & 5.00 & 250 & 89.74 & 56.23 & 0.83 \\ Processing Energy (X\({}_{2}\)) & kWh/ton & 0.65 & 2.10 & 1.38 & 0.47 & 0.48 \\ Moisture Content (X\({}_{3}\)) & \% & 2.10 & 15.0 & 8.72 & 3.19 & 0.22 \\ Gradation Adjustment (X\({}_{4}\)) & categorical & 0 & 2.00 & 0.96 & 0.69 & 0.61 \\ Grid CO\({}_{2}\) Factor (X\({}_{5}\)) & kg CO\({}_{2}\)/kWh & 0.24 & 1.05 & 0.61 & 0.21 & 0.27 \\ \hline \end{tabular}
\end{table} 
Table 1: Descriptive statistics of the engineered input variables (n = 482, post-cleaning).
# B-Line Detection and Localization in Lung Ultrasound Videos Using Spatiotemporal Attention
## 5 Experiment Results
### Effect of Video Length on B-Line Classification Performance  
Our B-line classification model (C2D+S+LSTM+T) was trained on the video lengths of one-second and was tested on the same video lengths with a performance over 80%. In a real clinical setting, however, the duration of the clips may vary (typically between 1 and 4 s). Additionally, the ability to use shorter videos can enable a more efficient translation to real time imaging. Therefore, we investigate the effect of various LUS video lengths on the model performance during the training and testing phases of the model.  
The maximum length of our LUS videos is four seconds. As a result, we divided these four-second videos into shorter videos of length \(l=\{1,2,3,4\}\) s. Results are presented in Table 5. Interestingly, when the model was trained on longer videos and then tested on shorter videos (For example, training = 4 s, and testing = 1 s), the performance decreased which means it needs more times for learning the video features. In contrast, training with shorter video lengths and testing with longer video lengths (For example, training = 1 s, and testing = 4 s) showed that one second of the four-second videos is enough for the model to find meaningful features during the testing. Further, when the same video length is used during the training and testing, the model performance is, in all cases, \(>\)83%.
# On the Optimal Design of Steel Shells with Technological Constraints
## 3 Conditions of Minimum Cost Plastic Design of Full Steel Cylindrical Shells
### Sufficient Optimality Conditions for the Full Shell Case  
The structural design is expressed through the shell thicknesses of the rings, \(e_{i}\). The shell under load \(p(x)\) is exactly on the verge of collapse. The collapse mechanism describes a displacement velocity field. Using the combined theorem of the limit analysis, we can write  
\[\int_{A}p(x)\dot{w}(x)\mathrm{d}A=\sum_{i=1}^{n}D_{i}\,, \tag{11}\]  
Let a neighboring design \(e_{i}^{*}=e_{i}+\delta e_{i}\) meet technological constraints so that it can at least sustain a given load. The upper bound theorem of the limit analysis implies  
\[\int_{A}p(x)\dot{w}(x)\mathrm{d}A\leq\sum_{i=1}^{n}\mathcal{D}_{i}^{*}=\sum_{ i=1}^{n}\mathcal{D}_{i}+\sum_{i=1}^{n}\delta e_{i}\frac{\partial\mathcal{D}_{i}} {\partial e_{i}}. \tag{12}\]  
The field of displacements rates \(\dot{w}_{x}^{(i)}\) is added to the dimensioning \(e_{i}\), but is only allowed for dimensioning \(e_{i}^{*}\) and therefore an inequality sign is established. The specific power of dissipation energy \(\mathcal{D}_{i}\big{[}\dot{w}(x)\big{]}\) is assumed to be a continuously differentiable function at \(e_{i}\). Thus, we have  
\[\sum_{i=1}^{n}\delta e_{i}\frac{\partial\mathcal{D}_{i}}{\partial e_{i}}\geq 0 \tag{13}\]  
Figure 4: The cylindrical shell formed by rings.  
Suppose that the collapse mechanism,\(\hat{w}(x)\), is such that  
\[\frac{\partial\mathcal{D}_{i}}{\partial e_{i}}=\left(\frac{dc}{de}\right)\begin{array} []{l}e=e_{i}\\ c=c_{i}\end{array}A_{i}+t_{i},\ i=1,\ \ldots,\ n, \tag{14}\]  
where \(t_{i}\), \(i=1,\)\(\ldots,\)\(n\), are constants and \(A_{i}\) is the surface of the midline of the ring with thickness \(e_{i}\). From (13) with (14) we have  
\[\sum\limits_{i=1}^{n}\delta e_{i}\left(\frac{dc}{de}\right)\begin{array}{l}e =e_{i}\\ c=c_{i}\end{array}A_{i}+\sum\limits_{i=1}^{n}\delta e_{i}t_{i}\geq 0,i=1,\ \ldots,\ n, \tag{15}\]  
Analyzing the previous relation, we obtain  
\[\frac{\partial\mathcal{D}_{i}}{\partial e_{i}}\leq\left(\frac{dc}{de}\right) \begin{array}{l}e=e_{i}\\ c=c_{i}\end{array}A_{i}\text{, for }e_{i}=e_{i,inf}, \tag{16}\]  
\[\frac{\partial D_{i}}{\partial e_{i}}\geq\left(\frac{dc}{de}\right)_{e=e_{i} }A_{i}\text{, for }e_{i}=e_{i,sup}, \tag{17}\]  
\[\frac{\partial D_{i}}{\partial e_{i}}=\left(\frac{dc}{de}\right)_{e=e_{i}}A_{ i}\text{, for }e_{i,inf}<e_{i}<e_{i,sup}. \tag{18}\]  
For the Inequalities (16) and (17) were \(\delta e_{i}t_{i}\leq 0\) and to obtain Equation (18) was \(t_{i}=0\). For the previous analysis, the Inequality (15) can be written in a stronger form  
\[\sum\limits_{i=1}^{n}\delta e_{i}\left(\frac{dc}{de}\right)\begin{array}{l}e =e_{i}\\ c=c_{i}\end{array}A_{i}\geq 0, \tag{19}\]  
which indicates that the design \(e_{i}\)* includes a higher cost than design \(e_{i}\).  
The relationships (16), (17), and (18) represent relative minimum cost.
## 4 Proposed Clustering Algorithm
### CH Selection Approach (Quantum)
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Node ID**} & \multirow{2}{*}{**Node State**} & \multirow{2}{*}{**Node Weight (W\({}_{\text{i}}\))**} & \multicolumn{2}{c}{**Inversion of Mean Value**} \\  & & & \(W_{\text{iter}}\) = 2 * \(W_{\text{i}}\) + \(W_{\text{avg}}\) \\ \hline
1 & \(|0000\rangle\) & 1.23 & 1.27 \\ \hline
2 & \(|0001\rangle\) & 1.20 & 1.30 \\ \hline
3 & \(|0010\rangle\) & 1.60 & 0.90 \\ \hline
4 & \(|0011\rangle\) & 2.10 & 0.40 \\ \hline
5 & \(|0100\rangle\) & 0.85 & 1.65 \\ \hline
6 & \(|0101\rangle\) & 0.90 & 1.60 \\ \hline
7 & \(|0110\rangle\) & 1.20 & 1.30 \\ \hline
8 & \(|0111\rangle\) & 0.90 & 1.60 \\ \hline
9 & \(|1000\rangle\) & 1.60 & 0.90 \\ \hline
10 & \(|1001\rangle\) & 0.70 & 1.80 \\ \hline
11 & \(|1010\rangle\) & 1.90 & 0.60 \\ \hline
12 & \(|1011\rangle\) & 1.70 & 0.80 \\ \hline
13 & \(|1100\rangle\) & 0.75 & 1.75 \\ \hline
14 & \(|1101\rangle\) & 0.78 & 1.72 \\ \hline
15 & \(|1110\rangle\) & 1.37 & 1.13 \\ \hline
16 & \(|1111\rangle\) & Null & Null \\ \hline \multicolumn{4}{c}{Average weight} & \multicolumn{1}{c}{W\({}_{\text{avg}}\) = 1.25} & \(W_{\text{iter,avg}}\) = 1.25 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Our quantum algorithm based on weighted target parameters, and its implementation. \[f(x)= \begin{cases}1,\;if\;the\;weight\;is\;highest\\ 0,\;if\;the\;weight\;is\;lowest\end{cases}  \tag{29}\]  
Since, [PERSON]'s algorithm based on weighted targets requires  
\[\sum_{i=1}^{n}\omega_{i}\;=1 \tag{30}\]  
it means the sum of all of the search target node weights should be one. To obtain the sum value of one, it is necessary to normalize the corresponding weight values. Normalization can be performed via the following formula:  
\[\omega_{i}=\frac{W_{inv}}{\sum_{i=1}^{n}W_{inv}} \tag{31}\]  
where \(n\) is the number of candidate nodes. From Table 3, we can see that our candidate nodes are 10 and 13. The sum of their inverted weights is 3.55. As a result, when the normalized weights of node 10 (=0.51) and node13 (=0.49) are added together, they equals one. As mentioned earlier, Equation (16) represents the initial state of the nodes. Our marked state is constructed as follows:  
\[|q\rangle=\sqrt{0.51}\big{|}1001\big{\rangle}+\sqrt{0.49}\big{|}1100\big{\rangle} \tag{32}\]  
Now, the iteration steps can be found with Equation (28) by applying the original [PERSON]'s algorithm, which comes from [48]:  
\[t=CI\left(\frac{\frac{\pi}{2}-\varphi}{\omega}\right)=CI\left(\frac{\arccos\sqrt {\frac{m}{N}}}{2\arccos\sqrt{\frac{m}{N}}}\right) \tag{33}\]  
where \(m\) = number of candidate nodes/CH nodes, and \(N\) = total number of maximum nodes. Table 4 shows that when comparing both iteration values, Equation (28) depicts a value that is closer to two. Therefore, it can be stated that the targets from [PERSON]'s weighted search algorithm are more perfect than the original [PERSON]'s search algorithm ones are.
## 3 Proposed Scheme
### The SOC Compression and the Second Layer of Data Embedding Processes  
After the first layer embedding of secret data, the stego index table is sent to the indicator-free SOC compression process. To improve the efficiency of SOC, the indicator-elimination technique proposed by [PERSON] et al. [31] is adopted. Two examples are applied to illustrate the SOC with an indicator-free method. The code length is set to four bits. However, the head and rear codes '0000' and '1111' are reserved for special usage. For a compressible index as shown in Figure 11, the coding is the same as the method introduced in Section 2.2, except that the label is started with '0001' instead of '0000'. Besides, four secret bits are appended to camouflage a regular VQ index. For an incompressible index as shown in Figure 12, no matched index can be found in the predefined range of '0001' to '1110'. One secret bit is embedded by putting a reserved code, '0000' for '0' and '1111' for '1'. Then, the actual index is appended behind. To avoid confusing in the decoding process, the head and the rear indices in the first layer of embedding are treated as incompressible indices. The overall scheme of the proposed SOC and second layer of data embedding is illustrated in Figure 13, where the code length of VQ compression is assumed to be eight bits. The algorithm for SOC compression and the second layer of data embedding is given as Algorithm 2. Note that the tracing path of the indices near boundary of the index table may encounter the out-of-boundary problem. For such a case, the tracing is stopped and it  
Figure 10: Embedding example 3: abnormal block.  
Figure 9: Embedding example 2: mismatched case.  
treats the index under processing as incompressible. For instance, the indices in the first column are always incompressible according to this rule, since the first index to be checked is missing.  
Figure 11: Embedding example 1: compressible index.  
Figure 12: Embedding example 2: incompressible index.
# The Usage of Virtual and Augmented Reality in Underwater Archeology
## 7 Conclusions  
The analysis carried out as part of the article showed that VR and AR technologies are used in underwater archeology for various purposes. They are both research tools dedicated to professional applications and support the educational process at various levels, including educating students. These technologies are also eagerly used as a tool for popularizing and disseminating research results in the field of underwater archeology. In the latter respect, they are eagerly used by museum institutions as a tool to enrich exhibitions. Such use also stimulates the development of tourism in coastal areas.  
The analyzed application examples confirmed that VR and AR technologies can provide significant support for underwater archeology activities, both at the research and educational levels, and are useful tools in popularizing the topic of underwater research. VR and AR help document cultural heritage and conservation work and increase its accessibility to audiences. The idea behind the use of these technologies is to offer both archaeologists and the wider public a new perspective on reconstructed archaeological sites, which enables archaeologists to analyze material obtained directly from the reconstructed site using a virtual site, and at the same time allows the wider audience to \"immerse\" themselves in a realistic exploration of selected archaeological sites. The main findings show that these technologies are particularly useful in hard-to-reach places, such as the deep sea, where time and skills play a significant role in reaching a given object. Additionally, it is worth noting that the use of these technologies in underwater archeology is carried out in accordance with market directions and expected future trends in technology development, including the use of a participatory approach and the design of user-oriented applications.  
Conceptualization, K.K. and B.K.; methodology, K.K.; validation, B.K.; formal analysis, K.K.; investigation, K.K.; writing--original draft preparation, K.K.; writing--review and editing, B.K.; visualization, K.K.; supervision, B.K. All authors have read and agreed to the published version of the manuscript. This research received no external funding.  
Not applicable.  
Data Availability StatementData are contained within the article.  
The authors declare no conflicts of interest.
## 2 Numerical Model  
### Model Geometry  
The use of numerical simulations to model the behavior of the piezocone probe in soil is a valuable tool in geotechnical engineering. It allows engineers to study the effects of different soil properties and boundary conditions on the probe measurements. In this article, the software Abaqus is employed as a critical tool in performing the simulations and analysis required for the study [24]. Figure 1 shows the model for a piezocone penetration simulation, which is a type of unit test used to determine the penetration resistance of a specific layer of soil under a particular pressure. The penetration resistance value obtained from this simulation corresponds only to the soil layer being tested and not to the entire soil profile. To expand on the statement, the pressure exerted by the overlying soil is known as the overburden pressure or overlying pressure. This pressure is caused by the weight of the soil above a certain point or depth. The overburden pressure increases with depth. To account for the effects of pore water pressure on soil behavior, it is common practice to use effective stress analysis.  
The standard size piezocone probe was considered in this research, and the diameter was approximately 0.036 m to simplify the calculation. The axisymmetric problem in the simulation involved dividing the soil into quadratic quadrilateral elements using reduced integration. The piezocone probe, on the other hand, was treated as a rigid body in the simulation, and its interaction with the soil was modeled using smooth contact. If it was necessary to compare with the experimental results, the influence of the friction coefficient was directly added to \(1.3\alpha_{\mathrm{c}}\) on \(N_{\mathrm{c}}\)[6]. The soil domain was extended at least 10 times the cone diameter (\(D\)) in the horizontal direction and 20 times the cone diameter (\(D\)) in the vertical directions, which was sufficient to avoid boundary effects during the penetration and dissipation stage. Fixed displacement boundary conditions were specified on the base and sides of the soil domain. Any excess pore pressure was allowed to dissipate at the soil surface only.  
To achieve steady conditions for the penetration resistance and excess pore pressure field, the penetrometers were first pre-embedded to a depth of 0.1 times the probe diameter (0.1 \(D\)), and then penetrated to a depth of 10 times the probe diameter (10 \(D\)) before entering the dissipation phase. Analysis of the results showed that a penetration distance of 6 times the probe diameter (6 \(D\)) was adequate for achieving steady conditions for both the penetration resistance and the excess pore pressure field. The mesh is shown in Figure 1. The mesh site sensitivity was tested and the mesh near the probe was 0.05 \(D\). A tube with a diameter of \(D/20\) was used to avoid serious mesh distortion, and the tube was treated as a rigid body. The interaction between the piezocone and soil was modelled with smooth contact. This method was originally presented by [PERSON] et al. [23] and [PERSON] et al. [21] and has the high potential to obtain an accurate result.
# Seasonal Shifts and Smart Stats: Improving Biodrying in Waste Management
## 5 Discussion
### Seasonal Variations in Drying Efficiency  
The significant variations in drying efficiency across the months (Figure 5) and seasons (Figure 6) provide valuable insights into the environmental factors affecting the biodrying process. The lower efficiencies observed during summer and fall months (July to October) suggest that ambient temperature and humidity play crucial roles in the biodrying process. This seasonal effect aligns with observations by [PERSON] et al. [12], who reported that environmental conditions significantly impact biodrying performance. Other authors observed similar findings during the biological treatment of waste [38; 39]. The highest average drying efficiency in spring (approximately 28%) compared to the lowest in the summer and fall (around 22%) indicates a need for seasonal adjustments in process parameters. This finding is particularly important for waste management facilities operating in regions with distinct seasonal variations. Operators might consider modifying aeration rates, retention times, or other operational parameters during warmer months to compensate for reduced efficiency.
# W-Band 4 th Order Waveguide Filter Based on Double Layer SU8 Microfabrication
## 2 Materials and Methods  
### Design of the Filter Structure  
The BPF operating at 90 GHz with an equal ripple relative bandwidth of 4.5 GHz has been designed. The designed filter has a passband RL of 20 dB and two transmission zeros at 85.5 GHz and 94.6 GHz, respectively. Based on the specifications, the external \(Q_{\text{ext}}\) and the non-zero coupling coefficients between resonators are calculated to be: \(Q_{\text{ext}}=17.64\), \(k_{12}=0.0476\), \(k_{23}=0.0436\), \(k_{34}=0.0476\), and \(k_{14}=0.0121\). The structure of the presented filter is designed according to its topology, as shown in Figure 1a. The fabricated filter consists of three stacked SU-8 layers. Layer 1 or 3 contains the cavities and the input or output coupling slots and layer 2 is the coupling irises layer, as shown in Figure 1b. To make the fabricated SU-8 chips easy to be measured, two types of pressure blocks are designed, as shown in Figure 1d,e.
## 3 Circuit Level Perspectives of JCRS RF Hardware
### Antenna Implementation
#### 3.2.1 Antennas for Radar
Hence, radar systems composed of MIMO antennas at the mmWave band are anticipated to accomplish high spatial resolution, optimized signal-to-noise ratio, decreased susceptibility to interference, enhanced positioning, and target discovery in comparison with conventional approaches [14]. A MIMO system includes several transmit antennas, each of which transmits an arbitrary waveform independently of the others. These signals may be received by each receiving antenna. Two sorts of MIMO radar systems are discernible [46]. The first is the monostatic MIMO radar, which employs collocated antennas and uses point targets similar to conventional radar systems. The second type of MIMO radar is distributed or bistatic and contains widely spaced antennas, each with a distinctive perspective of the target. The following presents a synopsis of their operating principles. Monostatic radar: The antenna elements are well-positioned to one another to detect indistinguishable target radar cross sections (RCS). As with a phased-array antenna, each radiator in this setup features an independent transceiver module and analog-to-digital converter. A phased-array antenna, on the other hand, simply transmits an impersonation of a transmission signal generated in the central waveform generator through each radiator. As each radiator in a MIMO radar system features its individual arbitrary waveform generator, every radiator produces a distinct waveform. The identification of the echo signal's source is also based on this particular waveform. Bistatic radar: Each antenna views the target through a distinct angle, in contrast with monostatic MIMO. As a result, every antenna on the target has a different RCS. Analyzing radar data on such targets necessitates far more intricate target models.
## 9 Results and Discussion
### Cold Start Penalty Scaling Analysis  
Cold Start Penalty Scaling Analysis evaluates how system performance deteriorates as the cost--measured in time or resource overhead--of initializing a new container (cold start) increases. This analysis involves systematically introducing artificial delays (ranging from 100 ms to 2000 ms) during cold starts and measuring the resulting impact on container utilization. The results, presented in Figure 13, show that the Hybrid model's performance  
Figure 11: Container state distribution across the four applications.  
degrades more gradually than the other methods across all application types. The most significant drop in utilization for the Hybrid model occurs in the PN and MM workloads, at approximately 12%. In contrast, some methods, such as OW, show up to a 20% degradation in similar scenarios. This more graceful decline in performance indicates that the Hybrid model's proactive strategy--combining pre-warming and buffer-aware scheduling--is effective across varying workloads and cold start penalties.
# An Efficient Task Autonomous Planning Method for Small Satellites
## 3 Methods  
Because of the limited computing resources on the satellite, the design of the framework must be compatible with the resources, requiring the program to be as simple and small as possible. At the same time, the planning of task requires high efficiency. In this paper, we propose a roll-planning framework of on-board autonomous task planning and a programming algorithm for rolling planning, in order to meet the need of real-time tasks. The framework was designed into three parts, rolling, programming algorithm, and re-planning, respectively.