esa-sceva/satcom-chunk-collection huggingface dataset:
# Multi-Feature Behavior Relationship for Multi-Behavior Recommendation
## 3 Preliminaries  
In our recommendation scenario, we utilize \(U=\{u_{1},\cdots,u_{i},\cdots,u_{l}\}\) and \(V=\{v_{1},\cdots,v_{i},\cdots,v_{l}\}\) to represent the set of users and items, respectively, where \(I\) and \(J\) denote the number of users and items. We further define the key concepts in multi-behavior recommendations as follows.  
**Multi-Behavior Interaction Tensor, \(X\)**. To model the user's multi-behavior relations, we create a three-dimensional vector, \(X\in R^{I\times J\times K}\). The number of behavior categories is denoted by the letter \(K\). Under the \(k\)-_th_ behavioral interaction type, \(x^{k}_{i,j}=1\) if \(u_{i}\) interacts with \(v_{j}\); otherwise, \(x^{k}_{i,j}=0\).  
**Multi-Behavior Interactive Sequence, \(S_{u}\)**. When considering a user \(u\), we define its multi-behavior history sequence as \(S_{u}=\{s^{1}_{u},s^{2}_{u},\cdots,s^{K}_{u}\}\), where \(s^{k}_{u}\) is the item interaction sequence of user \(u\) under behavior \(k\). We then have the item sequence \(s^{k}_{u}=\{v_{1},v_{2},\cdots,v_{|s^{k}_{u}|}\}\) for each behavior.  
**Multi-Behavior User-Item Interaction Graph, \(G\)**. We define a user-item multi-behavior relation graph as \(G=(U,V,E)\), where \(U\) and \(V\) are user sets and item sets, respectively and \(E\) are the set of edges of multi-type interactions. When there is an interaction \(x_{i,j}^{k}=1\) between \(u_{i}\) and \(v_{j}\) under specific behavior \(k\), there is an edge \(e_{i,j}^{k}\) in \(G\).  
**Task Formulation**. The interaction likelihood of user \(u_{i}\) and item \(v_{j}\) is predicted based on a given user multi-behavior sequence \(S_{u}\) and the multi-behavior user-item interaction graph \(G\) under target behavior type \(k\).
## 2 Related Work
### Gender Estimation  
A lot of progress has been achieved in the gender estimation topic, but it is still a challenging problem in the real-world environment. The literature about gender estimation comes under the umbrella of the authors of [59; 60]. Here, we will discuss some of those methods where the well-known classifiers are used for the gender estimation. As one of the very early methods, the authors of [61] used a fully connected two-layer neural network that learned from a limited number of near-frontal face images for gender classification. In [62], SVM classifiers were directly applied to image intensities. Similarly, AdaBoost was introduced instead of SVM classifier by keeping the same working pipeline [63]. Later on, a viewpoint-invariant model for age and gender estimation was suggested by [PERSON] and [PERSON] [64] which is robust to local scale rotations.  
A combination of human knowledge and a gait information-based gender classification system was provided by [PERSON] et al. [65]. An unconstrained face image benchmark for gender classification along with a high classification accuracy was presented in [9]. [PERSON] et al. [66] formed a semantic pyramid by extracting features from the full and upper body together with face regions from the image to recognize the gender and action. This method does not depend on the annotation value of a person's face and upper body to extract semantic features. In [67], the authors proposed a model where the first name of a person is used as a special feature and associates a name with facial appearance to recognize the gender of that person. At the same time, the authors showed that their method achieved higher accuracy in the task of gender recognition and demonstrated the potential in the use of face verification. In recent times, a generic framework for age and gender estimation was proposed in [46], where a hierarchical estimator was modeled based on extracted biologically inspired features. Besides, this method was formed to detect low-quality images due to a poor image background.  
The above efforts regarding gender estimation contributed a lot in this research area. However, the lion's share of these methods is only suitable for the applications with constraint images or have higher computational costs. Recently, a deep CNN-based approach was presented in [68]. It was pretrained with a huge unconstrained dataset and then fine-tuned on two other datasets to achieve a very good accuracy in the gender estimation task. Although, their method states a high accuracy, a lot of pretraining is required prior to evaluating the system. In our paper, we propose a system that will work comparatively well with unconstrained imaging conditions.
# New Space Engineering Design: Characterization of Key Drivers
## 2 Methodology
### Document Screening and Selection  
Documents obtained from the literature search using the defined keywords were filtered based on their publication date and their relevance to the space economy. Preliminary readings confirmed that the early 2000s are typically considered the beginning of New Space activities and practices; this period was, therefore, used as the starting point for the review. When examining specific topics or insights, more recent sources were always prioritized. Earlier works were retained if their content was still considered valid for understanding today's evolution or for contextualizing the historical roots of the New Space phenomenon. This approach also reflects the current landscape, in which many key actors are newly established companies. These entities are often cautious about disclosing sensitive information. They are primarily focused on achieving market success, which may limit the availability of recent research, particularly on emerging technologies or commercial strategies.  
The inclusion or exclusion of each document was determined through a methodical screening process, carried out in the following order: titles, abstracts, conclusions, and relevant references. This structured approach ensured that no source was excluded a priori, allowing the reliability of each document to be assessed regardless of its origin or typology. Each selected document was then recorded and organized on a structured Microsoft Excel  
Figure 1: Research methodology used to identify key design drivers and enablers in New Space engineering design. The selected design drivers and enablers are first categorized and subsequently contextualized within specific application domains.  
worksheet, with each document occupying a separate row. The table included columns for thematic area, publication year, and authorship, allowing for efficient sorting and filtering. This organizational step facilitated the removal of duplicate entries originating from separate search sessions.  
This process resulted in a comprehensive set of documents used to initiate the analytical phases of the study. The analyzed literature encompassed a broad range of sources, including both technical and non-technical materials, as well as economic analyses and industry reports related to general space sector dynamics.
# Study of Urban Unmanned Aerial Vehicle Separation in Free Flight Based on Track Prediction
## 3 Separation Calculation Model Based on Track Prediction
### Separation Calculation Model
#### 3.2.2 Collision Probability in Conflict  
When calculating separation, to ensure compliance with safety requirements in any case, the separation is calculated based on the most conservative head-on encounter mode.  
Figure 4: UAV gas model.  
The separation needs to cover not only delays in control command calculation and execution but also the positioning error, as well as the maneuvering distance required for conflict resolution. Let \(D\) be the candidate value for separation. It can be expressed by Equation (6):  
\[D-V_{\text{closure}}\times\left(t_{\text{sep}}+t_{\text{pilot}}\right)\leqslant d _{\text{avoid}}+\varepsilon_{1}+\varepsilon_{2} \tag{6}\]  
In the formula, \(D\) is the candidate value for separation; \(V_{\text{closure}}\) is the closure rate; \(t_{\text{sep}}\) is the response time of the air traffic control module; \(t_{\text{pilot}}\) is the response time of the flight control module; \(d_{\text{avoid}}\) is the maneuvering distance required for collision avoidance; and \(\varepsilon_{1},\varepsilon_{2}\) are the position prediction errors for each UAV.  
The collision probability between two aircrafts is as shown in Equation (7):  
\[Prob(\text{collision}|\text{conflict})=P\Big{\{}D-V_{\text{closure}}\times \left(t_{\text{sep}}+t_{\text{pilot}}\right)\Big{\}}\leq d_{\text{avoid}}+ \varepsilon_{1}+\varepsilon_{2} \tag{7}\]  
Let \(D^{\prime}\) be the actual required distance between two UAVs, as shown in Equation (8):  
\[D^{\prime}=V_{\text{closure}}\times\left(t_{\text{sep}}+t_{\text{pilot}} \right)+d_{\text{avoid}}+\varepsilon_{1}+\varepsilon_{2} \tag{8}\]  
The probability density function of \(D^{\prime}\) is as shown in Equation (9):  
\[f(x)=\frac{1}{\sqrt{2\pi}\sigma}e^{-\frac{(x-x)^{2}}{2\sigma^{2}}} \tag{9}\]  
In the formula, \(\mu\) is the mean of \(D^{\prime}\) and \(\sigma\) is the standard deviation of \(D^{\prime}\).  
Collision probability in conflict means the probability that \(D^{\prime}\) exceeds D, as shown in Equation (10):  
\[Prob(\text{collision}|\text{conflict})=\int_{D}^{+\infty}\frac{1}{\sqrt{2\pi} \sigma}e^{-\frac{(x-\mu)^{2}}{2\sigma^{2}}}dx \tag{10}\]
# S-PDR: SBAUPT-Based Pedestrian Dead Reckoning Algorithm for Free-Moving Handheld Devices
## 5 Results and Discussion
### Indoor Test
\begin{table}
\begin{tabular}{c c c c c} \hline \multirow{2}{*}{**Test \#**} & \multicolumn{2}{c}{**Final Positional Error (m)**} & \multicolumn{2}{c}{**Average Position Error across Trajectory**} \\ \cline{2-5}  & **S-PDR** & **Xsens MTI G-710** & **S-PDR** & **Xsens MTI G-710** \\ \hline
1 & 11.51 & 27.9 & 5.79 & 17.53 \\ \hline
2 & 10.52 & 31.58 & 4.61 & 21.97 \\ \hline
3 & 6.30 & 34.8 & 3.4 & 20.13 \\ \hline
4 & 5.9  
For the case of S-PDR all the tests had a final positional error ranging between 4.63 and 11.51 m, with one sigma confidence interval in the 5.9 to 10.02 m of error range. The average duration of this test was 3 min with an average trajectory length of 290 m, hence the relative final position error is found to be ranging between 1.5% to 3.5% of the trajectory.
## 2 Materials and Methods
### Effect of the Generated (Reactive) CAP Species on the Biofilms  
#### 2.5.1 Viable Cell Density Reduction due to UV Light  
In order to comment on the relative contribution of UV light to the CAP inactivation efficacy, Petri dishes with mature _L. monocytogenes. Listeria monocytogenes_ and _Salmonella_ Typhimurium model biofilms were covered with a quartz disc (Alfa AesarTM quartz disc, \(\upomega=76.2\) mm, Fisher Scientific, Merelbeke, Belgium) prior to energizing the plasma power source. The quartz disc prevents all plasma species, apart from UV photons, to interact with the biofilm samples [29]. Therefore, log-reductions obtained following CAP treatment of the quartz-covered biofilms were deemed to be solely the result of the generation of UV light. Viable plate counts were used to determine the remaining cell density following 10 min of CAP treatment and the corresponding log-reduction values were calculated based on this remaining cell density and the initial cell density of the untreated model biofilms. Finally, the log-reduction results obtained within the presented research were compared with those previously obtained within the research of [PERSON] et al. [17] following 10 min of CAP treatment without prior implementation of a quartz disc. Two independent biological replicates (\(n=2\)) were used for this assay.  
In order to determine the remaining viable cell density, the biofilms were removed from the surface using previously mentioned cell scraping method. Serial decimal dilutions (in 0.85% (_m/v_)NaCl solution) of the obtained cell suspension were prepared and plated on agar plates. Brain Heart Infusion Agar (BHIA, BHI supplemented with 14 g/L biological agar, VWR Chemicals, Oud-Heverlee, Belgium) was used for _Listeria monocytogenes_, while Tryptic Soy Agar (TSA, TSB supplemented with 14 g/L biological agar) was used for _Salmonella_ Typhimuriumium. For each of the serial dilutions, three drops of 20 \(\upmu\)L were plated. Before counting the colonies, BHIA and TSA plates were incubated for at least 24 h at 30 or 37 \({}^{\circ}\)C, respectively.
## 3 System Model
### Energy Model  
The following equation is used to calculate the transmitting, receiving and aggregating data of energy consumption:  
\[E_{tr}(l)=\left\{\begin{array}{ll}E_{\text{elec}}&+l\colon\varepsilon_{fs} \cdot d^{2}&d\leq d_{0},\\ E_{\text{elec}}&+l\colon\varepsilon_{mp}\cdot d^{4}&d>d_{0},\end{array}\right. \tag{1}\]  
\[E_{ag}(l)=lE_{da}\]  
where d is the distance from the source node to Sink; \(d_{0}=\sqrt{\frac{\varepsilon_{fs}}{\varepsilon_{mp}}}\); \(E_{tx}(l)\), \(E_{rx}(l)\), \(E_{ag}(l)\) for aggregating 1 bit of data; \(E_{da}\) reflects the energy use; \(\varepsilon_{fs}\) and \(\varepsilon_{mp}\) represent the signal amplifier power magnifications in free-space and multi-channel attenuation models, respectively; and Emelec denotes the energy usage of a wireless transceiver circuit in transmitting and  
Figure 1: Overall proposed architecture.  
receiving data. If \(\mathrm{d}\leq\mathrm{d}_{0}\), the network's communication model is a free-space method; otherwise, it is a multi-channel attenuation method modeled by Equation (2):  
\[\begin{array}{l}\sum\limits_{i=1}^{N}E_{i}=\sum\limits_{i=1}^{K}\left[E_{m} \left(\sum\limits_{j=1}^{K+1}I_{ij}-\sum\limits_{j=1}^{K}I_{ij}\right)+\sum \limits_{j=1}^{K+1}E_{ij}+\sum\limits_{j=1}^{K}E_{m}I_{i_{j}}\right]\\ \quad=E_{sm}\sum\limits_{i=1}^{K}I_{iK+1}+\sum\limits_{i=1}^{K}\sum\limits_{j= 1}^{K+1}E_{ij}+E_{m}\sum\limits_{i=1}^{K}\sum\limits_{j=1}^{K}I_{ij}\\ \quad=\sum\limits_{i=1}^{K}(E_{sm}I_{iK+1}+E_{iK+1})\\ \quad+\sum\limits_{i=1}^{K}\sum\limits_{j=1}^{K}\left(E_{rc}I_{ij}+E_{ij} \right)m\sum\limits_{i=1}^{K}\left(E_{sm}I_{iK+1}+E_{iK+1}\right)\\ \quad+\sum\limits_{i=1}^{K}\sum\limits_{j=1}^{K}\left(E_{n}I_{ij}+E_{ij} \right)\end{array} \tag{2}\]  
such that,  
\[\sum\limits_{j=1}^{K+1}I_{ij}-\sum\limits_{j=1}^{K}I_{ji}\geq 0\]  
for \(i=1:K\) and from Equation (3)  
\[\sum\limits_{j=1}^{K+1}I_{ij}-\sum\limits_{j=1}^{K}I_{ij}\leq M\sum\limits_{j =1}^{K}I_{jK+1} \tag{3}\]  
for \(i=1:K\), Equation (4) is  
\[\sum\limits_{j=1}^{K}I_{jK+1}\geq I_{\min} \tag{4}\]  
which becomes Equation (5)  
\[I_{ij}\leq\log\left(1+\frac{E_{ij}d_{ij}^{-2}}{\xi}\right) \tag{5}\]  
for \(i=1:K,j=1:K+1\), which is rewritten as Equation (6):  
\[I_{ij}\geq 0;E_{ij}\geq 0 \tag{6}\]  
for \(i=1:K,j=1:K+1\).  
Equations (5) and (6) are used to compute the maximum energy bounds ranging from ESP \({}^{1}\)/4 1 Joule to ESP \({}^{1}\)/4 15 Joule and fewer data bounds ranging from Imin \({}^{1}\)/4 0 to Imin \({}^{1}\)/4 170 bits, respectively. The network is randomly distributed with 100 nodes with Erc \({}^{1}\)/4 500 \(\upmu\)J, Esm \({}^{1}\)/4 1 mJ, and M \({}^{1}\)/4 20 bits based on the relationship between Equations (5) and (6). The problem with the minimum data bound in Equation (6) is that it converges faster than the issue with high energy bound in Equation (5).
# Use of Autochthonous Lactobacillusli to Increase the Safety of Zgougou
## Materials and Methods
### Determination of Antioxidant Activity  
Antioxidant activity was measured on juice and seeds obtained after 24 h of fermentation (spontaneous or inoculated with autochthonous lactobacilli), as well as on zgougou pudding (obtained using spontaneously or lactobacilli-driven fermented juice of Aleppo pine's seeds) and pudding obtained without addition of fermented juice (control). For determination of antioxidant activity of seeds debris obtained after 24 h of fermentation, methanolic extract was prepared mixing 3 g of seeds and 30 mL of methanol (80%, _v/b_). After keeping this mixture under nitrogen flux for 5 min at 20 \({}^{\circ}\)C, the mixture was centrifuged (4032\(\times\)_g_, 20 min, 4 \({}^{\circ}\)C) and the supernatant was subjected to analysis [54]. For determination of antioxidant activity of puddings, ethanolic extract was prepared mixing 10 g of pudding and 20 mL of absolute ethanol (99.5%, _v/v_) to form a fine smooth slurry [55]. After centrifugation (3000\(\times\)_g_, 10 min, 4\({}^{\circ}\)C) the supernatant represented the zgougou pudding extract (concentration of 500 g/L). Dilutions of the supernatant were made at various concentrations (100, 200, 300, 400 g/L) with 75% ethanol.  
Antioxidant activity was estimated in terms of FRAP and scavenging activity towards the radical DPPH. For assay of FRAP, 200 \(\mu\)L of sample (juice/methanolic extract of seeds/ethanolic extract of pudding) were mixed with 200 \(\mu\)L of 0.2 M sodium phosphate buffer (pH 6.6) and 200 \(\mu\)L of potassium ferricyanide (1%, _v/b_). The reaction mixture was incubated at 50 \({}^{\circ}\)C for 20 min. Reaction was stopped by adding 200 \(\mu\)L of trichloroacetic acid (10%, _w/v_). After centrifugation (8000\(\times\)_g_, 10 min, 4 \({}^{\circ}\)C), 500 \(\mu\)L of the collected supernatant were added of 100 \(\mu\)L ferric chloride (0.1%, _w/v_) and 400 \(\mu\)L of demineralized water. After incubation (10 min, 25 \({}^{\circ}\)C), the absorbance of ferrous ion was measured at 700 nm. Ascorbic acid (1%, _w/v_) was used as a positive control [56].  
The free radical scavenging activity against DPPH was measured according to the method described by [PERSON] et al. [57]. In detail, 167 \(\mu\)L of sample (juice/methanolic extract of seeds/ethanolic extract of pudding) were mixed with 167 \(\mu\)L of DPPH (2.5 g/L) and 667 \(\mu\)L of methanol (80%, _v/v_). The reaction mixture was incubated in dark up to 120 min at 25 \({}^{\circ}\)C. The absorbance of the reaction mixture was measured at 517 nm at 0, 10, 20, 30, 60 and 120 min. Two positive controls were used: BHT (0.45 g/L in methanol 80%) and ascorbic acid (1%). A reaction mixture consisting just of 167 \(\mu\)L of DPPH and 833 \(\mu\)L of methanol (80%) was always inserted as the blank. The free radical scavenging activity was calculated as follows:  
\[\text{DPPH scavenging activity (\%)}=[(\text{blank absorbance -- sample absorbance})/\text{blank absorbance}]\times 100 \tag{1}\]
# Deterministic Coresets for \(k\)-Means of Big Sparse Data +
## 8 Conclusions  
We proved that any set of points in \(\mathbb{R}^{d}\) has a \((k,\varepsilon)\)-coreset which consists of a weighted subset of the input points whose size is independent of \(n\) and \(d\), and polynomial in \(1/\varepsilon\). Our algorithm carefully selects \(m\) such that the \(m\)-means of the input with appropriate weights (clusters' size) yields such a coreset.  
This allows us to finally compute coreset for sparse high dimensional data, in both the streaming and the distributed setting. As a practical example, we computed the first coreset for the full English Wikipedia. We hope that our open source code will allow researchers in the industry and academia to run these coresets on more databases such as images, speech or tweets.  
The reduction to \(k\)-means allows us to use popular \(k\)-means heuristics (such as Lloyd-Max) and provable constant factor approximations (such as \(k\)-means++) in practice. Our experimental results on both a single machine and on the cloud shows that our coreset construction significantly improves over existing techniques, especially for small coresets, due to its deterministic approach.  
Figure 8: Allocated memory (y-axis) grows logarithmically during streaming coreset construction. The Zig-zag patterns caused by the binary merge-reduce tree in Figure 1.  
Figure 6: Error (y-axis) box-plots for real-data sets, streaming computation model.  
Figure 7: Error (y-axis) box-plots for wikipedia dataset, distributed computation for k = 32, 64 and 128.  
We hope that this paper will also help the community to answer the following three open problems:  
1. Can we simply compute the \(m\)-means for a specific value \(m\in k^{O(1/\varepsilon)}\) and obtain a \((k,\varepsilon)\)-coreset without using our algorithm?
2. can we compute such a coreset (subset of the input) whose size is \(m\in(k/\varepsilon)^{O(1)}\)?
3. Can we compute such a smaller coreset deterministically?  
Conceptualization and Formal Analysis D.F. and A.B.; funding acquisition, D.F.; Code writing A.B. All authors have read and agreed to the published version of the manuscript.  
This research was funded by BSF/NSF Grant Number: 2014627 and by GIF 2408- 407.6 Young Scientists' Program Contract No.: I-1186-407.9-2014.  
The authors declare no conflict of interest.
# Measurement-Driven Synthesis of Female Digital Mannequin Using Convex Sub-Volumes
## 1 Introduction
### Research Strategy and Scope  
This manuscript applies to digital model synthesis, the well-known practice in the artistic drawing and sketching domain of approximating a human or animal body by a set of ellipsoids. This set of ellipsoidal primitives effectively captures the pose and demeanor of the (static) female model. It does not concern itself with the details. A bijective mapping tailor measurements vs. ellipsoid set is computed. This mapping allows the synthesis of a female mannequin that satisfies a set of tailor measurements. It also allows extraction of the tailor measurements of a given mannequin. We then use a derivative convex primitive, called _cashion_ (the convex hull of two ellipsoids), to complement the approximation of the female human body. We do not seek to arrive at a mesh representation of the body. Therefore, our approach does not incur the expenses of mesh cleaning, repairing, convex decomposition, etc. The approximation of the female mannequin by using a set of convex primitives is accepted by some physics engines (e.g., [5]) to simulate garment-body interaction. Notice that we do not, in this manuscript, model garment-body interaction.  
Our manuscript does not attempt the automated synthesis of digital mannequins from images, videos, or point clouds (neither static nor dynamic) through feature extractions and scalings, among other procedures. Our manuscript does not address mannequin kinetics or motion, nor recognition/classification of subject actions or activities. For readers interested in these topics, references [6;7] may provide useful insight. The interest of our effort is to use a convex primitive approximation of a digital mannequin and tailor measurements in order to create other digital individuals with diverse sizes. Our manuscript executes interpolation among sizes of ellipsoid-based mannequins by finding and using bijections between sets of tailor measurements and ellipsoid-based mannequins. Our manuscript addresses static (as opposed to motion) scenarios.  
Our industrial sponsor has female mannequins and garments as its priority. This fact explains why this publication does not address male digital mannequins. Future efforts will do so.  
The problem that this manuscript addresses is formulated as:  
**Given**  
1. _Me_: A set of measurements \(Me=[He,Sh,Br,Wa,Hi]\) that describe the shape of a female body. With \(He\): Height, _Sl_: Shoulder width, _Br_: Breast perimeter, _Wa_: Waist perimeter, and \(Hi\): Hip perimeter.  
**Goal**  
1. _B_: 3D mannequin approximation of a female body that satisfies a particular set of tailor measurements \(Me\). \(B\) is a set of convex volumes.