esa-sceva/satcom-chunk-collection huggingface dataset:
# Effect of the Umbrella Arch Technique Modelled as a Homogenized Area above a Cross Passage
## 2 Project Description and Geological Conditions
### Modelling the Staged Construction  
The initial step was implemented to generate the effects of geostatic stress. The main tunnel was then excavated using the full-face technique. A face pressure P\({}_{\text{f}}\) = 0.252 MPa was applied at the front face of the excavation, and its value was calculated using the formula mentioned by [24; 25]. The excavation is modelled by successive deactivation of the tunnel volume elements; during this phase a certain percentage of stress relaxation \(\lambda\) is applied, while the lining is activated and deconfinement is completed after each excavation step. Subsequently, the homogenized medium representing the umbrella arch pipes is activated and the excavation of the cross passage is done in semi-section. The excavation length of the main tunnel was set at 1.5 m, which is the same length as the segments used in situ. For the cross passage, the excavation length was 1 m, which is also the length used in the project when excavating the cross passage. Steps 1-62 were performed to simulate the construction of the main tunnel and steps 63-116 were performed to model the activation of the umbrella arch and the construction of the lateral opening and the cross passage. The percentage of relaxation \(\lambda\) was calculated using the C-Lambda software. For simplification reasons, only one soil layer was considered in the 3D modelling, namely the marl layer.  
To assess the settlement reduction provided by the umbrella arch technique, three models with different pre-support lengths were performed; the lengths of the umbrella arch elements will be as follows: L = 8 m, L = 10 m, L = 12 m.
# Hash Indexing-Based Image Matching for 3D Reconstruction
## 3 The Proposed Method
### Hash Matching
#### 3.3.1 Creating a Hash Table  
Descriptor matching is still a challenging task in large-scale 3D reconstruction. Although some novel approaches have been proposed for accelerating descriptor matching in image matching, these methods need further improvements regarding how they deal with high-resolution images. To reduce the computational costs of descriptor matching, in this section, we propose the creation of a big hash table to accelerate image matching via hash indexing, then boost the computational efficiency of image-based 3D reconstruction. Specifically, let \(htable\) be a hash table for the given descriptor collections \(d=\{d_{1},\cdots,d_{N}\}\), and the \(htable\) is defined as (in C-Plus-Plus language):  
\[htable=\{int\ N;float\ sum[3];float\ avg[3];float\ squsum[3];float\ std[3]\} \tag{7}\]  
where \(N\) is the number of feature points, \(sum[3]\) is used to store the sum of Haar wavelet coefficients of all feature descriptors and \(squsum[3]\) is utilized to store the squared sum of Haar wavelet coefficients of all feature descriptors. Let \(c_{k1}\), \(c_{k2}\), and \(c_{k3}\) represent the Haar wavelet coefficients for the descriptor \(d_{k}\in d\), which can then be calculated using the following formulas,  
\[\left\{\begin{aligned} c_{k1}&=e_{k1}+e_{k2}-e_{k3}-e _{k4}\\ c_{k2}&=-e_{k1}+e_{k2}-e_{k3}+e_{k4}\\ c_{k3}&=e_{k1}-e_{k2}-e_{k3}+e_{k4}\end{aligned}\right. \tag{8}\]  
Additionally, \(e_{k1},e_{k2},e_{k3}\), and \(e_{k4}\) are the sum of descriptor elements and are defined as  
\[e_{k1}=\sum_{i=0}^{i=3}\sum_{j=0}^{j=3}d_{k}[i*8+j] \tag{9}\]  
\[e_{k2}=\sum_{i=0}^{i=3}\sum_{j=4}^{j=7}d_{k}[i*8+j] \tag{10}\]  
\[e_{k3}=\sum_{i=4}^{i=7}\sum_{j=0}^{j=3}d_{k}[i*8+j] \tag{11}\]  
Figure 4: Flowchart of hash matching. Where (**a**) denotes the input feature descriptors, (**b**) represents the structure of the hash table in C-plus-plus language, (**c**) represents the pipeline of hash matching, and (**d**) denotes the output indexes of feature correspondences.  
\[e_{k4}=\sum_{i=4}^{i=7}\sum_{j=4}^{j=7}d_{k}[i*8+j] \tag{12}\]  
Upon finding the values of \(c_{k1}\), \(c_{k2}\), and \(c_{k3}\), the members of _htable_ can be computed as  
\[sum[index]=\sum_{k=0}^{k=N}c_{k1},index=0,1,2 \tag{13}\]  
\[squsum[index]=\sum_{k=0}^{k=N}c_{k1}*c_{k1},index=0,1,2 \tag{14}\]  
\[std[index]=\sqrt{\frac{squsum[index]}{N}-sum[index]*sum[index]},index=0,1,2 \tag{15}\]  
\[avg[index]=\frac{sum[index]}{N},index=0,1,2 \tag{16}\]  
To this end, we can create a big hash table based on Haar wavelet transformation for the input feature descriptor collections for fast indexing in the image matching stage.
## 3 Methodology
### Adaptive Response Surface Method  
The optimization routine makes use of adaptive response surface optimization to optimize numerical models based on a multitude of measured data points. The routine is designed to handle multiple-output time series data for the thermal case and frequency data for the modal case [24; 27]. The optimization procedure can be divided into the following steps:  
1. Starting reference simulation points are selected, and an adapted object function is defined by taking into account the difference between the FE model values and the optimal target values.
2. The FE model is replaced by an interpolation-based meta-model of response surfaces to decrease optimization time, but this replacement remains an accurate approximation.
3. The optimization routine is run on the specific object function to find the minimum of the meta-model. An optimization routine is used according to a genetic algorithm computation to find the optimum speed of the complex numerical model.
4. The estimated parameter values are used as input parameters for an improved FE model that corrects the meta-model.
5. Only the points that are closer to the minimum are used to form the response surface (pan & zoom function).  
The optimized response model is not built from a pre-defined number of design experiments but is adapted and refined during the optimization routine by the pan & zoom command [24]. The method automatically calculates the parameter influence sensitivity and sequentially resolves multiple sets with major influencing parameters and fixed parameter values for minor influencing parameters. Afterward, the second parameter set is optimized, and an influence check is then performed for all parameter sets until the results converge.
# Single-Layer Pressure Textile Sensors with Woven Conductive Yarm Circuit
## 5 Conclusions  
This paper presented a case of a single-layer pressure sensor fabricated by interweaving the conductive wool yarns and spacer thread (nonconductive) together in order to create the one-layer pressure sensor. The conductive wool yarns are converted from nonconductive yarns by being impregnated in CNT inks. Two electrodes of the sensor used two highly conductive threads. The electrical characteristics of the sensor are analyzed through experiments. Unlike the conventional pressure sensors (multi-layer structure), this sensor is thin (0.4 mm), extremely lightweight, and has a small hysteresis (5.5%). We also demonstrated the application of the proposed sensor for detecting the grasping force via the smart glove. Such sensors can be utilized to sense the grasp action in robot control or human motion recognition.  
All authors have read and agree to the published version of the manuscript. Conceptualization, G.K., C.C.V., and J.K.; methodology, G.K., C.C.V., and J.K.; software, C.C.V.; validation, G.K., C.C.V., and J.K.; formal analysis, G.K., and C.C.V.; investigation, G.K.; resources, G.K., C.C.V., and J.K.; data curation, G.K., C.C.V., and J.K.; writing--original draft preparation, G.K., and C.C.V.; writing--review and editing, G.K., C.C.V., and J.K.; visualization, G.K., C.C.V., and J.K.; supervision, J.K.; project administration, J.K.; funding acquisition, J.K.  
This research was funded by the National Research Foundation of Korea (NRF) grant number 2019R1A2C2005933 and Korea Display Industry Association(KDIA) grant number P0002397. The APC was funded by the National Research Foundation of Korea and Korea Display Industry Association.  
This research was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2019R1A2C2005933), and the Competency Development Program for Industry Specialists of the Korean Ministry of Trade, Industry, and Energy (MOTIE) operated by the Korea Institute for Advancement of Technology (KIAT) (No. P0002397, HRD program for Industrial Convergence of Wearable Smart Devices).  
The authors declare no conflict of interest.  
Figure 9: Smart glove application of the sensor, consisting of (**a**) structure of the sensing finger on glove, (**b**) hardware platform, (**c**) grasping a water cup, and (**d**) the change in signal amplitude when the subject grasps the different weights.
# Block-Wisely Supervised Network Pruning with Knowledge Distillation and Markov Chain Monte Carlo
## 2 Related Works
### Knowledge Distillation  
The basic idea of knowledge distillation is to first train a large network (teacher network) and transfer the knowledge learned in the large network to a smaller network (student network) [17; 18]. For example, [PERSON] et al. [17] introduced a compression framework for knowledge distillation that trains the student network with the soft labels of the teacher network. There has been an increasing interest in applying knowledge distillation to network pruning. KD can also be applied to the middle layers of the teacher network where rich representation information can be extracted. [PERSON] et al. [19] used knowledge distillation and NAS to design a general-purpose compression scheme that reduces the computation and memory consumption of generators in Conditional Generative Adversarial Networks. [PERSON] et al. [20] proposed a knowledge-distillation-based FitNet to guide training of the student network by exploiting outputs of the teacher network and features of intermediate layers. In this paper, the baseline and pruned networks are treated as teacher and student networks, respectively. We divide both networks into distinct blocks and use block-wise representations of the teacher network to supervise student network pruning by sampling candidate subnets via MCMC.
# Challenges and Potential in Increasing Lutein Content in Microalgae
###### Abstract  
Research on enhancing lutein content in microalgae has made significant progress in recent years. However, strategies are needed to address the possible limitations of microalgae as practical lutein producers. The capacity of lutein sequestration may determine the upper limit of cellular lutein content. The preliminary estimation presented in this work suggests that the lutein sequestration capacity of the light-harvesting complex (LHC) of microalgae is most likely below 2% on the basis of dry cell weight (DCW). Due to its nature as a structural pigment, higher lutein content might interfere with the LHC in fulfilling photosynthetic functions. Storing lutein in a lipophilic environment is a mechanism for achieving high lutein content but several critical barriers must be overcome such as lutein degradation and access to lipid droplet to be stored through esterification. Understanding the mechanisms underlying lipid droplet biogenesis in chloroplasts, as well as carotenoid trafficking through chloroplast membranes and carotenoid esterification, may provide insight for new approaches to achieve high lutein contents in algae. In the meantime, building the machinery for esterification and sequestration of lutein and other hydroxyl-carotenoids in model microorganisms, such as yeast, with synthetic biology technology provides a promising option.  
Keywords:lutein; storage capacity; lipid droplets +
Footnote †: journal: Microorganisms
# Situation-Aware Survivable Network Design for Tactical Environments
## 3 Proposed Algorithm
### Branch Node Based Routing Algorithm
#### 3.2.2 Operation Process  
Figure 6 shows a simple example network to illustrate the operation process of the proposed algorithm. The branch node-based routing algorithm searches the top-\(k\) (\(k=3\) in our example) paths that meet the requirement of a tactical packet by broadcasting an RREQ message. The number on each path indicates its ranking, and the existence of the branch node in the upper side is verified by depending on the value of the RREQ count field. The nodes behind the branch node receive the RREQ message with the RREQ count value of 2; therefore, the destination node can recognize the existence of the branch node and forward  
Figure 4: RREQ message format.  
Figure 3: A branch node and the rank of each path in the network.  
the RREP message toward it. However, in case of the conventional routing algorithm, the destination node forwards the RREP message toward the path of rank 1 because it discards a duplicate RREQ message.  
```
/* RREQ packet arrival part */ /* 1. Check prev_addr is my_addr */ ifaddr_equal(my_addr,prev_addr)==truethen recv_pkt_destroy(); FOUT; endif /* 2. Increment the RREQ count value at branch node */ /* and update route table for branch node at dest_node*/ ifaddr_equal(my_addr,dest_addr)==truethen  route_table_update(next_hop_addr,hop_count); else ifmy_count \(\geq\)req_countthen req_ptr \(\rightarrow\)req_count++;  route_entry_ptr \(\rightarrow\)req_count =req_ptr \(\rightarrow\)req_count; else  route_entry_ptr \(\rightarrow\)req_count =req_ptr \(\rightarrow\)req_count; endif  broadcast_rreq(); endif /* RREP packet arrival part */ /* 3. Update route table for branch node at src_node */ if(reached_src)&(req_count \(>\) 1)then  route_table_update(next_hop_addr,hop_count); endif
```  
**Algorithm 2** Pseudocode of branch node-based routing  
The path repair process with the branch node-based routing algorithm is illustrated in Figure 7. If a node on the primary path senses that connection is lost to the next hop, it starts the route repair process. If local repair is attempted, the proposed routing scheme can easily and quickly find an alternative route near the primary path. However, in the case of the conventional routing scheme, it may fail to recover an end-to-end path with local repair because it constructs a single path. Consequently, the source has to construct a new path after it receives a route error (RERR) message. Because this takes a long time, it causes inevitable performance degradation, such as long delays or packet drop. Even if local  
Figure 5: Modified RREQ message format.  
repair is successful, it is hard to guarantee that the new path can satisfy the requirements of tactical traffic.
# The Legal Framework for Space Debris Remediation as a Tool for Sustainability in Outer Space
## 2 Why Space Debris Remediation? The Facts
### The Legal Framework for Space Activities
However, no specific requirements for states as to how to exercise their activities in a manner that would ensure that the standard of care towards of activities of other states are provided. Thus, the legal framework provides for some general direction for co-operation between the users of outer space but concrete instruments on how to ensure sustainability need to be formulated in more detail. In fact, the treaties on space law neither expressly prohibit the creation of space debris nor impose an obligation on states and their space actors to remove space objects from orbit. Mitigation measures have so far only been adopted as voluntary, non-binding instruments and have been partly adopted in the national laws of some states [25]. In sum, it can be stated that a general obligation to protect the environment of outer space results from the common interest of the community of states to access and use outer space. If a narrow interpretation of the theory of erga omnes obligations is followed, it is the currently 107 State parties to the OST [32] which represent the community having a common interest in the protection of the usability of outer space. If the view is followed, that due to the broad support and the principle-based character of some of its norms, the Outer Space Treaty has at least partly customary character, it can be argued that the 107 State Parties represent the global community so that the global community has a legal interest in the environmental protection of outer space., but a concrete, binding way of action for SDR cannot be derived from existing space law [41].
## 4 Analytical Analysis of Two-Port Interconnections
### Sum of Fractional-Order Integrator and Integer-Order Differentiator Responses  
The last considered combination of integer-order and fractional-order response is shown in Figure 9. The resulting frequency response is quite similar to the previous case:  
\[K_{I\_D2}(s)\ =\ A_{1}\tau_{1}s+\frac{A_{2}}{\tau_{2}s^{\alpha}} \tag{22}\]  
again, we can modify it to the complex form:  
\[K_{I\_D2}(\omega)\ =\ \frac{1}{\alpha^{\alpha}\tau_{2}}\bigg{\{}A_{2}\cos \bigg{(}\frac{\alpha\pi}{2}\bigg{)}+j\bigg{[}\omega^{1+\alpha}\tau_{1}\tau_{2} A_{1}-A_{2}\cos\bigg{(}\frac{\alpha\pi}{2}\bigg{)}\bigg{]}\bigg{\}} \tag{23}\]  
and we can express the corresponding magnitude and phase responses:  
\[\big{|}K_{I\_D2}(\omega)\big{|}\ =\ \frac{1}{\alpha^{\alpha}\tau_{2}}\sqrt{ \bigg{[}A_{2}\cos\bigg{(}\frac{\alpha\pi}{2}\bigg{)}\bigg{]}^{2}+\bigg{[} \omega^{1+\alpha}\tau_{1}\tau_{2}A_{1}-A_{2}\cos\bigg{(}\frac{\alpha\pi}{2} \bigg{)}\bigg{]}^{2}} \tag{24}\]  
\[\varphi_{I\_D2}(\omega)\ =\ \tan^{-1}\bigg{[}\frac{\omega^{1+\alpha}\tau_{1} \tau_{2}A_{1}-A_{2}\cos\bigg{(}\frac{\alpha\pi}{2}\bigg{)}}{A_{2}\cos\bigg{(} \frac{\alpha\pi}{2}\bigg{)}}\bigg{]} \tag{25}\]  
The 3D plot in Figure 10 also reports the significant global minimum of transfer (as expected). However, the sides of both slopes are opposite with respect to the previous case. The cut in phase projection shows the initial phase value (low-frequency corner) dependence on the order (start between -90\({}^{\circ}\) and 0\({}^{\circ}\)).  
Figure 8: The 3D plot of studied ideal transfer response (18) in dependence on value of the \(\alpha\) variable: (**a**) magnitude vs order vs frequency dependence, (**b**) phase vs order vs frequency dependence.
## 5 Convolutional Neural Network for Indoor Positioning
### CNN-Based Regression Positioning Method  
In the previous research about classification-based fingerprint positioning, the area was divided into small grids, and the fingerprints were mapped to the reference point (RP) in the grid [57; 58]; however, this approach is not suitable for large areas because of the extensive number of classes it needs to be divided into, and this will greatly increase the model complexity in deep learning-based fingerprint positioning. Meanwhile, a straightforward classification method of fingerprint positioning first defines RPs by collecting features at different points and then finding the similarity between the target feature and different RPs. This heavily depends on the number and density of RPs because of the limited training space; it is hard to reach sub-meter level accuracy in large areas. In contrast, the regression method can overcome the discontinuity of RPs and has the potential to reach higher accuracy, while it is also insensitive to the size of areas. From these concerns, we use the regression method rather than the classification.
## 4 Results  
### Feature 8  
According to the ASL-derived data, feature 8 is a circular ridge of 60-70 cm in height and about 12 m in diameter, located north-east of Laganisi village and some hundred meters from the above-cited cave with the same name (Figure 1, feature 8). The circular structure is almost outcropping, covered by thin soil and some vegetation and by a modern field division dry wall which crosses the structure along the N-W S-E direction. It testifies for the ancient origin of its construction (Figure 3A,B). As is clearly visible in the ASL-derived images and profiles, the eastern margin of the mound is sharp while the western side decreases gradually, with a maximum elevation of about 50 cm above the ground level. A circular hole is visible on the western side of the modern wall (Figure 4).  
Due to the presence of the modern wall in the middle, GPR investigations were carried out over two separated sectors, located, respectively, on the western and on the eastern sides of the modern structure.  
On the southern sector of the mound, two anomalies of irregular shape separated by the modern wall, are visible from about 20-30 cm to about 110-120 cm from the ground level. The intensity of reflected signals is compatible with a stone core of about 9 m in diameter with an embossed top (Figure 5, anomalies 1-2; Figure 6A,B).  
Figure 3: Ortophoto (**A**) and slope visualization (**B**) of feature 8; Ortophoto (**C**) and slope visualization (**D**) of feature 10.  
Figure 4: Simple Local Relief Model visualization of feature 8 (**left**) and interpreted profiles (**right**) (radius = 20 px). Dotted lines and associated letters a-a1, b-b1 and c-c1 indicate the position of the profiles. The topographic surface profiles from ALS data are in red while the profiles of the underlying stone cap from GPR data are in blue.  
Figure 5: GPR slices of feature 8 at different depth from the ground level.  
A third circular anomaly is visible on the western sector of the mound from about 30-50 cm from the ground level (Figure 5, anomaly 3). The circular margin of the structure is not well visible and can be recognized only on the south-eastern sector of the mound between 25-40 cm from the ground level (Figure 5, anomaly 4), where it appears to be marked by stones and boulders possibly arranged in a row.  
DTMs of both the topographic surface (from ALS data) and of the stone cap (from GPR scans) allowed us to produce a 3D visualization of both surfaces (Figure 6C). From those we calculated the overall volume of the mound and the volume of the capstone where the tomb was presumably inserted. The estimated volume of the stone cap corresponds to about 39 m\({}^{3}\), while the overall mound volume is roughly 56 m\({}^{3}\). This means that the volume of the earth deposits covering the stone cap accounts for approximately 17 m\({}^{3}\).
# Applications of Probabilistic Forecasting in Smart Grids: A Review
## 2 Probabilistic Forecasting Models
### Artificial Neural Network-Based Probabilistic Forecasting
#### 2.3.2 Examples of Probabilistic Forecasting Models Using RNN  
RNN can also be combined with long short-term memory (LSTM) units. LSTM consists of some memory blocks that are recurrently connected. Each block consists of three multiplicative units including input gate, output gate and forget gate. The input gate memorizes either the new information or the previous states of the network. The forget gate disregards the irrelevant and unnecessary information obtained from the past. The output gate extracts the important information from the memory. In this way, unnecessary information is forgotten and only necessary ones are kept within the network [25].  
Reference [25] is an example of research developing two RNN-LSTM-based probabilistic forecasting models. The first model is a parametric model and quite is similar to MDN. It first tries to exact a PDF of the uncertain parameter. Then, an RNN-LSTM network is trained to find the optimal values of PDF parameters. The other model is, however, a non-parametric probabilistic forecasting model that is integrated with RNN-LSTM. It employs the QR method with the objective to predict the quantiles of the uncertain parameter. The network is trained to minimize the quantile loss by minimizing their pinball loss.
## 3 Theory and Method
### Bidirectional GRU Layer  
A Bidirectional Gated Recurrent Unit is a recurrent neural network (RNN) variant that considers past and future contextual information in time series data. GRU is a gated loop unit that models long-term dependencies in sequence data. In a traditional one-way RNN, information flows in only one direction, from the past to the future. The bidirectional GRU incorporates an additional reverse layer, enabling information propagation in both the forward and backward directions, thus enhancing the capture of contextual information within sequence data.