esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
This article concerns the efficiency of the distribution system with different strategies--\"Pull\" or \"Push\"--and different sizes of distribution network in terms of when products produced by the manufacturing plant are sent to distribution warehouses. The article hypothesized that the choice of how to replenish stocks in these warehouses--\"Pull\" or \"Push\"--and the choice of the degree of centralization of the distribution network (number of warehouses) were two decision problems that should be considered together. This hypothesis was confirmed. A simulation model was developed to conduct simulations for different scenarios (different demand distributions--Gaussian or Gamma different demand fluctuations and the timeliness of replenishing inventories in warehouses). With more expensive goods and greater sales fluctuations, there was a certain tendency towards centralizing storage and using the Pull strategy. The choice of a given strategy had a significant impact on the costs of logistics processes and on the profitability of enterprises. The cost savings ranged from 17% to 54%. The average share of distribution costs in the sales value was 6%. In some cases, it was over 10% (the level of profitability of industrial enterprises In Poland). Choosing the right strategy could, in some cases, change profits by 20%. In most cases, the most cost-effective strategy was a flexible Pull system and centralized storage, which is consistent with real-life business cases.  
Published in: \"Pull\" vs. \"Push\" and the Degree of Centralization of Warehousing in the Field of Physical Distribution with a Special Focus on the Polish Market.  
19 February 20226 March 20251 April 20251 April 202519 February 20  
The choice of strategy has an impact on the parameters of logistics and production processes and, consequently, on their costs. Since the choice can result in different levels of logistics customer service, it can affect the level of sales.  
The aim of this research, the results of which are presented in this article, was to examine the possible impact of a given strategy on economic efficiency and what other factors may affect this efficiency. The problem of choosing between the Pull and Push strategy was combined with the problem of choosing the size of the distribution network (degree of centralization of storage). The aim of these analyses was to show that these two decision-making problems should be considered together rather than separately.
## 1 Introduction  
Pores are a fatal defect in welding, as they reduce the strength of the joint. During laser beam welding, pores result from instabilities of the capillary [1; 2; 3; 4; 5; 6]. Numerical studies [4; 7] and experimental results [3; 8; 9] show that pores result from a collapse of the capillary. Based on this knowledge, there are several approaches to prevent the formation of pores by means of stabilization of the capillary: e.g., by means of power modulation [10], beam shaping [11; 12], multi-spot applications [13], very high feed rates [3; 14], or spatial beam oscillation [15; 16].  
Industrial sheet metals are often coated with oil or other lubricants, so-called pre-lubes, which are required for subsequent forming processes. It is generally known that these surface conditions lead to pores within the weld metal [17; 18]. Figure 0(a) shows the cross section of a full penetration weld, welded in overlap configuration, of two uncleaned AA6016 sheets, which were coated in Drylube EI. Figure 0(b) shows the cross section of a full penetration weld in two AA6016 sheets, which were cleaned by etching the surface to ensure that no lubricants were present. The thickness of the two sheets was 1.2 mm.  
The cross section in Figure 1a shows obvious pores. Almost no pores can be found in the cross section of Figure 1b, where no lubricants were present during welding. The ratio between the summarized area of pores in the cross section of the weld and the total area of the cross section of the weld yields the porosity, which quantifies the number of pores in welds. Figure 1b compares the porosity of welds in cleaned and uncleaned samples. The height of the bars represents the average porosity of the three analyzed welds, while the length of the error bars represents the range between the minimum and maximum measured values.  
Figure 1c shows a decrease in porosity by about 80% when the surface of the samples is cleaned by etching before they are welded. This proves the significant influence of the presence of a lubricant on the formation of pores during welding of uncleaned sheet metals, which is generally accepted in laser beam welding. However, the formation process of the pores is not well investigated, as most studies cover the stability of the capillary [1; 2; 3; 4; 5; 6; 19]. It has yet to be proven whether the formation of pores during laser beam welding of pre-lube-coated sheets results directly from the evaporation of the lubricants or whether the evaporating lubricants lead to capillary fluctuations that result in the generation of pores in an indirect manner. This article reports on investigations of the formation of pores during full penetration welding of uncleaned pre-lube-coated metal sheets by means of in-situ X-ray imaging in order to identify the origin of the pore formation.
## 3 Finite element model (FEM) Analysis of Sound Transmission Loss of the ABH Circular Plate  
### FEM Model of Vibro-Acoustic Coupling  
The sound insulation problem of an ABH circular plate is a vibro-acoustic coupling process. Assuming that the fluid medium is an ideal non-viscous medium, the Helmholtz equation for the steady-state sound field can be expressed as:  
\[\
abla^{2}p(x,y,z)-k^{2}p(x,y,z)=-j\rho_{0}\omega q(x,y,z) \tag{15}\]  
where \(\
abla^{2}\) is the Laplace Operator; \(k\) is the wave number, \(k=\omega/c\); \(p(x,y,z)\) is the sound pressure; \(\omega\) is the angular frequency; \(\rho_{0}\) is the density of the fluid. In the finite element calculation, the interpolation function is selected to represent the sound pressure:  
\[p=\{\mathbf{N}\}^{T}\{\mathbf{p}\} \tag{16}\]  
where \(\{\mathbf{p}\}\) is the column vector of the sound pressure of the nodes and \(\{\mathbf{N}\}\) is the column vector of the shape function.  
The pressure of the sound acting on the structure can be regarded as an additional normal load. Thus, the dynamic equation of the structural model can be written as:  
\[\Big{(}\mathbf{K}_{s}+j\omega\mathbf{C}_{s}-\omega^{2}\mathbf{M}_{s}\Big{)} \cdot\{u_{i}\}+\mathbf{K}_{c}\{p_{i}\}=\{F_{si}\} \tag{17}\]  
where \(\mathbf{K}_{s}\), \(\mathbf{M}_{s}\), \(\mathbf{C}_{s}\) are the stiffness matrix, mass matrix, and damping matrix of the unconstrained part of the structural mesh; \(\mathbf{K}_{c}\) is the coupling stiffness matrix; \(\{F_{si}\}\) is the excitation load, which includes the contribution of the constraint portion of the structure, the known load and the external sound pressure load \(p\) perpendicular to the surface of the structure.  
At the location where the fluid and the structure are coupled, the vibration velocity in the normal direction of the structure is equal to the vibration velocity in the normal direction of the fluid. At the acoustic-solid coupling boundary, the vibration velocity of the structure can be regarded as the additional velocity input of the sound, and the acoustic equation can be written as:  
\[\Big{(}\mathbf{K}_{a}+j\omega\mathbf{C}_{a}-\omega^{2}\mathbf{M}_{a}\Big{)} \cdot\{p_{i}\}-\omega^{2}\mathbf{M}_{c}\{u_{i}\}=\{F_{ai}\} \tag{18}\]  
where \(\mathbf{M}_{c}\) is the coupled mass matrix; \(\{F_{ai}\}\) is the excitation load, which contains the known sound pressure, the sound source in the acoustic field, and the contribution of the vibration velocity boundary.  
Equations (17) and (18) can be written as a coupled equation in the form of a matrix:  
\[\left(\left[\begin{array}{cc}\mathbf{K}_{s}&\mathbf{K}_{c}\\ 0&\mathbf{K}_{a}\end{array}\right]+j\omega\left[\begin{array}{cc}\mathbf{C} _{s}&0\\ 0&\mathbf{C}_{a}\end{array}\right]-\omega^{2}\left[\begin{array}{cc}\mathbf{ M}_{s}&0\\ -\rho_{0}\mathbf{K}_{c}^{T}&\mathbf{M}_{a}\end{array}\right]\right)\left[\begin{array}{c }u_{i}\\ p_{i}\end{array}\right]=\left[\begin{array}{c}F_{st}\\ F_{at}\end{array}\right] \tag{19}\]
# Weed Detection and Classification with Computer Vision Using a Limited Image Dataset
## 2 Materials and Methods
### Shape Features  
The contour of the plant was determined based on the mask using the Canny edge detection algorithm, and the convex hull lines were also drawn, as can be seen in Figure 4. From the contour, parameters such as area, hull area, and solidity were derived.  
To calculate the hull and hull area, the convex hull of the plant contour was measured. The convex hull is the smallest convex polygon that can enclose all points of the contour. The hull area represents the total area enclosed by this convex polygon.  
Solidity is a metric that quantifies the compactness of an object's shape relative to its convex hull. It is calculated as the ratio of the object's area to the area of its convex hull. Solidity values range from 0 to 1, where a solidity of 1 indicates a completely solid shape without concavities, while a lower solidity value suggests the presence of concavities or indentations in the shape.  
Figure 3: The main steps of image processing: from RGB images to features.
# Optimal Base Station Density of Dense Network:
## 3 The Expected Link Rate of the SmBS Tier  
In this section, we derive the expected ergodic link rate of the SmBS tier and analyze what and how factors of deployment influence the link rate. For the SmBS network that is dense and irregular, there are two important questions in the analysis of link rate. They are the user association and interference distribution in the SmBS tier. User association determines the distance between the user and its serving BS, and the amount of data that can be offloaded to the SmBS tier. Interference distribution plays the key role in SINR.
# Inactivation of Thioglucidase from _Sinapis alba_ (White Mustard) Seed by Metal Salts
###### Abstract  
The glucosionolates which are specialized plant metabolites of _Brassica_ vegetables are prone to hydrolysis catalyzed by an endogenous enzyme myrosinase (thioglycoside hydrolase, thioglucidase) that exists in Brassica plant tissue causing volatile isothi cyanates release. Currently existing literature data on the inactivation of myrosinase is insufficient in particular for use in the analysis of volatile and odor compounds in vegetables rich in glucosionolates. In this study, the impact of different metal salts in effective inactivation of enzyme activity was investigated by solid-phase microextraction (SPME) and GC/MS system in aqueous samples and kohlrabi matrix. A saturated solution of calcium chloride which is commonly used to stop enzyme activity in plant tissue inactivates the myrosinase-glucosinolate system. However, even without the participation of myrosinase, it changes the reaction pathway towards nitrile formation. The model experiment shows that optimum efficiency in inhibition of the enzyme system shows iron(III) ions, silver ions, and anhydride sodium sulfate resulting in no volatile products derived from glucosionolates. However, in the kohlrabi matrix, the strongest enzyme inhibition effect was observed for silver salt resulting in no volatile products, also both anhydrous Na\({}_{2}\)SO\({}_{4}\) and saturated CaCl\({}_{2}\) solution seem to be useful inhibitors in flavor studies.  
myrosinase; thioglucidase; enzyme inactivation; glucosionolates hydrolysis; kohlrabi; isothi cyanates; nitriles; SPME
## 4 Cluster Formation Algorithm Evaluation  
All sensor nodes have been configured with a FoV vertex angle of \(\theta\) = 60 \({}^{\circ}\)and R\({}_{\mathrm{S}}\) of 20 m. A sensing field spanning an area of 120 m \(\times\)120 m has been used. Sensor densities were varied to study the cluster formation from sparse to dense random deployments. Figures illustrate the average results of 50 independent running tests whereas each test corresponds to a different random deployment. Once a random deployment is defined, cluster formation is obtained from node location, angle of orientation and FoVs of nodes, using the described algorithm whose complexity is O(N logN). Furthermore, as it was mentioned before, each node sends a packet to the sink in the bootstrap phase, then the sink notifies each first member via one packet his membership set for that cluster (phase 3) and then the first-members notify cluster nodes about their cluster membership and any related parameter. Thus, the average overhead of the algorithm is forwarding N packets from the nodes to the sink and forwarding N\({}_{\mathrm{C}}\) packets from the sink to first-members and forwarding N\({}_{\mathrm{C}}\) (\(\mu_{\mathrm{Csize}}-1\)) packets from first-members to cluster nodes; where N is the number of nodes, N\({}_{\mathrm{C}}\) is the average number of clusters and \(\mu_{\mathrm{Csize}}\) is the average cluster size. So the total overhead will be: N + N\({}_{\mathrm{C}}\)+ N\({}_{\mathrm{C}}\)(\(\mu_{\mathrm{Csize}}-1\)) packets. The maintenance overhead is N\({}_{\mathrm{C}}\)(\(\mu_{\mathrm{Csize}}-1\)) beacons every keep-alive period, where the keep-alive period can be a multiple of the sensing duty-cycle period.
## 2 Materials and Methods
### Description of the Walls and Concrete Mix Design  
The walls measured 14 m \(\times\) 2.75 m \(\times\) 0.80 m and were cast between the last week of August 2020 and the first week of September 2020. During this period, minimum and maximum temperatures ranged from 10.9 to 19.2 \({}^{\circ}\)C. Removal of formwork was conducted two days after casting.  
Two reference walls, WR1 and WR2, were produced without the addition of SAPs. The second reference (WR2) contained the additional water (normally to be stored in the SAPs) on top. As such, the effect of adding water could be explored without the link to internal curing. Three other walls, WS1, WS2, and WSC, were constructed with the incorporation of SAPs. WS1 used a commercial SAP (called SAP1) to mitigate autogenous shrinkage. WS2 was built with an in-house-developed SAP (SAP2) designed to promote crack sealing and partially reduce autogenous shrinkage when cracking occurs. WSC was built with a combination of SAPs SAP1 and SAP2, representing an ideal wall with the ability to mitigate autogenous shrinkage and promote self-sealing of cracks.  
The concrete used for WR1 had a strength class of C35/45. It was designed to limit crack width to 0.3 mm and included 16 mm diameter reinforcement with 96 mm spacing. The other walls (WS1, WS2, and WSC) were built with concrete of strength class C30/37. Similarly, these walls were designed to limit crack widths to 0.3 mm and included 16 mm diameter reinforcement with 107 mm spacing (as shown in Figure 2). All walls were built on reinforced concrete slabs made from the same concrete used for WR1. These slabs were produced three months before the walls were constructed.  
Figure 1: Aerial view (from Google Maps) of the testing site. The black scale bar on the bottom left of the figure amounts to 10 m. The five 14 m long walls are shown on the left-hand side. The description of the wall can be found in the following section.  
The following materials were used to make the mixtures researched in this study: (1) CEM III-B 42.5N--LH/SR from CBR, Zeebrugge, Belgium; (2) polycarbonate superplasticizer: Tixo, 25% concentration, manufactured by BASF, Waterloo, Belgium; (3) modified polycarbonate superplasticizer: Sika-Viscoflow 26, supplied by SIKA, Nazareth, Belgium; (4) sea sand 0/4 showing a mass absorption of 0.4%; and (5) limestone 2/20 with a mass absorption of 0.5%, next to the SAPs (please refer to the next section for details) and water. All concrete mixtures were designed with the aim of producing materials that meet consistency class S4 in terms of workability. The specific mixture designs for each wall type can be found in Table 1, listing the overall materials used next to the total and effective water-to-cement ratios.
# FocalMatch: Mitigating Class Imbalance of Pseudo Labels in Semi-Supervised Learning
## 3 Materials and Methods
### FocalMatch
Therefore, the unsupervised loss \(L_{u}\) of our method can be formulated as:  
\[L_{u}=\frac{1}{\mu B}\sum_{b=1}^{\mu B}\mathbb{1}\left(max(p_{m}(y|\alpha(u_{b })))\geq\tau)FL(p_{t}),\right. \tag{6}\]  
\[FL(p_{t})=-(1-p_{t})^{\gamma}log(p_{t}),p_{t}=\left\{ \begin{array}{ll}p_{m}(y |\mathcal{A}(u_{b}))&if~{}~{}\hat{p}_{m}(y|\alpha(u_{b}))=1\\ 1-p_{m}(y|\mathcal{A}(u_{b}))&otherwise,\end{array} \right. \tag{7}\]  
where \(u_{b}\) is unlabeled data, \(\alpha\) and \(\mathcal{A}\) refer to weak data augmentation and strong data augmentation, respectively. \(p_{m}(y|\alpha(u_{b}))\) and \(p_{m}(y|\mathcal{A}(u_{b}))\) are the predicted probability distri  
Figure 2: The loss curves of cross-entropy loss and focal loss with different \(\gamma\) values. Note that when \(\gamma=0\), the focal loss is identical to cross-entropy loss. butions on weakly-augmented and strongly-augmented samples, respectively. The former one is also the soft label of the unlabeled sample. In addition, \(\hat{p}_{m}(y|\alpha(u_{b})\) are the pseudo labels generated from soft labels where the confidence of a specific class is higher than \(\tau\) (i.e., the hyperparameter that defines the threshold). The supervised loss \(L_{s}\) is the same as FixMatch [15]:  
\[L_{s}=\frac{1}{B}\sum_{b=1}^{B}H(y_{b},p_{m}(y|\alpha(x_{b}))), \tag{8}\]  
where \(x_{b}\) is the labeled data, \(y_{b}\) is the corresponding label, and \(H\) is the standard cross-entropy loss. The overall loss of FocalMatch is:  
\[Loss=Ls+\lambda L_{u} \tag{9}\]  
where \(\lambda\) is another hyperparameter that defines the weight of unsupervised loss \(Lu\). The detailed algorithm of our method is shown in Algorithm 1. ```
1:Input:\(\mathcal{X}=\{(x_{b},y_{b}):b\in(1,\ldots,B)\},\mathcal{U}=\{u_{b}:b\in(1, \ldots,\mu B)\}\)\(\triangleright\)\(\mathcal{X}\) is labeled set, \(\mathcal{U}\) is unlabeled set
2:for i = 1 to max iteration do
3:\(L_{s}\), \(L_{u}\gets 0\)
4:for b = 1 to B do
5: labeled_prediction \(\gets p_{m}(y|\alpha(x_{b}))\)\(\triangleright\) Class distribution on weakly augmented \(x_{b}\)
6:\(L_{s}\) += cross_entropy_loss(\(y_{b}\), labeled_prediction)\(\triangleright\) Equation (8)
7:endfor
8:for b = 1 to \(\mu B\)do
9:if max(\(p_{m}(y|\alpha(u_{b}))\)) \(\triangleright\) Confidence of a class is higher than \(\tau\)
10: pseudo_label \(\leftarrow\) argmax(\(p_{m}(y|\alpha(u_{b}))\))
11: unlabeled_prediction \(\gets p_{m}(y|\mathcal{A}(u_{b}))\)\(\triangleright\) Class distribution on strongly augmented \(u_{b}\)
12:\(L_{u}\) += focal_loss(pseudo_label, unlabeled_prediction)\(\triangleright\) Equation (6)
13:endif
14:endfor
15:\(L_{s}\leftarrow\frac{L_{s}}{B}\), \(L_{u}\leftarrow\frac{L_{u}}{\mu B}\)
16: Loss \(\leftarrow\)\(L_{s}+\lambda L_{u}\)
17: Model Update
18:endfor
```  
**Algorithm 1** FocalMatch Algorithm
###### Abstract  
In this contribution, we assess, for the first time, the tightly combined real-time kinematic (RTK) with GPS, Galileo, and BDS-3 operational satellites using observations from their overlapping L1-E1-B1C/L5-E5a-B2a frequencies. First, the characteristics of B1C/B2a signals from BDS-3 operational satellites is evaluated compared to GPS/Galileo L1-E1/L5-E5a signals in terms of observed carrier-to-noise density ratio, pseudorange multipath and noise, as well as double-differenced carrier phase and code residuals using data collected with scientific geodetic iGMAS and commercial M300 Pro receivers. It's demonstrated that the observational quality of B1C/B2a signals from BDS-3 operational satellites is comparable to that of GPS/Galileo L1-E1/L5-E5a signals. Then, we investigate the size and stability of phase and code differential inter-system bias (ISB) between BDS-3/GPS/Galileo BIC-L1-E1/B2a-L5-E5a signals using short baseline data collected with both identical and different receiver types. It is verified that the BDS-3/GPS/Galileo ISBs are indeed close to zero when identical type of receivers are used at both ends of a baseline. Moreover, they are generally present and stable in the time domain for baselines with different receiver types, which can be easily calibrated and corrected in advance. Finally, we present initial assessment of single-epoch tightly combined BDS-3/GPS/Galileo RTK with single-frequency and dual-frequency observations using a formal and empirical analysis, consisting of ambiguity dilution of precision (ADOP), ratio values, the empirical ambiguity resolution success rate, and the positioning accuracy. Experimental results demonstrate that the tightly combined model can deliver much lower ADOP and higher ratio values with respect to the classical loosely combined model whether for GPS/BDS-3 or GPS/Galileo/BDS-3 solutions. The positioning accuracy and the empirical ambiguity resolution success rate are remarkably improved as well, which could reach up to approximately 10%\(\sim\)60% under poor observational conditions.  
BDS-3; GPS; Galileo; tightly combined model; differential inter-system bias (ISB); real-time kinematic (RTK) +
Footnote †: journal:  
Article
## 3 Experiments
### ESP32 Power Consumption  
In this subsection, we focus on measuring how much energy the ESP32 microcontroller consumes in a typical operation including active and idle (sleep) states. Our goal is to estimate the daily energy consumption (24 h cycle) and how long the system can operate autonomously while connected to a standard power bank, which is a critical measurement for sustainable IoT deployment.  
Methodology and Results  
The oscilloscope measures from each phase are presented in Table 4. Based on these measurements, we can calculate the energy consumption of the ESP32 [35] over a day.  
Firstly, we have to determine the active time per day. The ESP32 is active for 0.1 s every 10 s, leading to 8640 active transmissions per day (since there are 86,400 s in a day). Now, calculating the energy use for a day.  
* Active energy for BLE: \[E_{a}=80\,\mathrm{mA}\times\frac{0.1}{3600}\,\mathrm{h}\times 8640=19.2\, \mathrm{mAh}/\mathrm{d}\] (1)
* Idle/sleep energy: \[E_{i}=0.01\,\mathrm{mA}\times\frac{86391.6}{3600}\,\mathrm{h}=0.24\,\mathrm{mAh }/\mathrm{d}\] (2)  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Feature** & **Value** \\ \hline Active BLE current & 80 mA \\ Idle/sleep current & 15 uA \\ Idle with DHT11 sensor current & 250 uA \\ Transmission time & 0.1 s per transmission \\ Transmission interval & Every 10 s \\ \hline \hline \end{tabular}
\end{table}
Table 4: ESP32 current consumption.  
* DHT11 sensor energy: \[E_{s}=0.25\,\mathrm{mA}\times\frac{0.1}{3600}\,\mathrm{h}\times 8640=0.06\, \mathrm{mAh}/\mathrm{d}\] (3) So finally, the total energy use over a day is: \[E=E_{a}+E_{i}+E_{s}=19.2\,\mathrm{mAh}/\mathrm{d}+0.24\,\mathrm{mAh}/\mathrm{d }+0.06\,\mathrm{mAh}/\mathrm{d}\] (4) \[E=19.5\,\mathrm{mAh}/\mathrm{d}\] (5)  
In order to get a feeling of the viability of the ESP32 remote node, used as an IoT device, we can construct a hypothesis. For example, we have a 10,000 mAh power bank available to be used as a power supply for the ESP32 with the sensor. This type of power supply is pretty common, cheap, and easy to use. Knowing that the ESP32 with the sensors can operate with a consumption of 19.5 mAh per day, we can estimate how long the setup will last by dividing the capacity of the power bank by the daily consumption.  
\[\frac{10000\,\mathrm{mAh}}{19.5\,\mathrm{mAh}/\mathrm{d}}=512.82\,\mathrm{d} \tag{6}\]