esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results  
A method was developed to immobilize the polyols onto the polyVBC support by reaction with sodium hydride. Excess ethylene glycol in dioxane provided the highest yield while immobilization of glycerol and pentaerythintol was done in NMP because of their low solubility in dioxane. Elemental analyses and FTIR spectra showed all -CH\({}_{2}\)Cl groups reacted. Reaction of polyols with DECP is known to give neutral phosphate esters [14]. The optimum procedure for directly preparing the monoprotic ligands was determined to be phosphorylating the polyols with DECP in pyridine containing 10% DMAP. Monoesters have been prepared with DMAP [15]. pEG1M, pGlyM, and pPenatM all have nearly equal acid and phosphorus capacities (Table 1), indicating that they consist of monoprotic phosphoric acid groups. As expected, DPA has an acid to phosphorus ratio of 2:1. The theoretical phosphorus capacities of monophosphorylated glycol, glycerol, and pentaerythintol are 3.36, 3.04, and 2.67 mmol/g, respectively; the experimental values for the latter two are within experimental error of theory, while the value for pEG1M is lower by 0.5 mmol/g. The FTIR spectrum of pPentaM is representative with the hydroxyl band at 3400 cm\({}^{-1}\) and the acidic -OH at 2300 cm\({}^{-1}\) (Figure 2). The P=O band appears at 1265 cm\({}^{-1}\) in the diethyl phosphate and its conversion to the monoprotic pPentaM shifts the P=O band to 1224 cm\({}^{-1}\). The significance of the shift will be discussed. Table 2 shows the order of divalent ion affinities for the three monoprotic phosphates is Pb(II) \(>\) Cd(II) \(>\) Cu(II) \(>\) Zn(II), thus following the polarizability order set by the Misono softness parameter [16]. The order is the same as the diethyl esters, but with far higher affinities for all ions (the \begin{table}
\begin{tabular}{l c c c} \hline \hline
**Resin** & **Acid Capacity (mmol/g)** & **Phosphorus Capacity (mmol/g)** & **Acid/Phosphorus** \\ \hline DPA & 9.65 & 4.71 & 2.05 \\ pEG1M & 2.23 & 2.82 & 0.79 \\ pGlyM & 2.97 & 2.85 & 1.04 \\ pPentaM & 2.61 & 2.81 & 0.93 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Acid and phosphorus capacities of phosphonic acid and monoprotic polymers. Figure 2: FTIR spectrum of pPentaM. diester pEG1 had no affinity for the divalent ions even at pH 2) [10]. Though the three monoprotic phosphates operate through a common -OP(O)(OH)(OEt) ligand, they have different ionic affinities, with pPentaM > pGlyM > pEG1M; this order is maintained with the trivalent ions, with Fe(III) > Lu(III) > La(III) > Al(III). Figure 3 illustrates the results for pPentaM. Introducing an acidic P-OH group thus enhances significantly the metal ion affinity from acidic solutions compared to sorption by the neutral diethyl esters which have lower metal ion affinities due to H\({}^{+}\) competition for the binding sites; e.g., though the neutral pGly has a high affinity for La(III) from 0.01 M nitric acid, it complexes only 11% from 1.0 M HNO\({}_{3}\), while pGlyM complexes 90%.
# A Novel Method Based on Topological Perception Theory for 3D Landmark Building Model Retrieval
## 5 Proposed Method
### Quotient Distance-Angle Histogram
#### 5.2.1 Voting Interval Improvement  
In theory, when establishing the voting interval, the inner distance in a non-concave plane is expected to be equal to the Euclidean distance, resulting in a quotient distance of 1. However, since the image is composed of discrete points, the inner distance is limited by the tolerance, which leads to computations that do not follow a straight path on the image, resulting in a quotient distance typically exceeding 1. As an illustration, consider Figure 6: Each intersection represents a pixel centroid. Assuming that the tolerance is \(\sqrt{5}\), the Euclidean distance from A to C is AC, the inner distance is AB + BC, and the quotient distance is the quotient of AB + BC and AC. Obviously, the quotient distance is not 1.  
Although there may exist points with quotient distances exceeding 1 within concave structures, for the majority of landmark building models characterized by \"stout\" structures, most point pairs in the model tend to have quotient distances close to 1, which can lead to confusion with non-concave graphs. Therefore, this paper seeks to find the maximum quotient distance value that can be obtained in non-concave graphs. This determination is essential for controlling the voting results by increasing the weight of point pairs that exceed the maximum value. The value of the maximum quotient distance in non-concave graphs is closely related to the tolerance (as show in Figure 6, where it can be seen that, for the case where \(\sqrt{5}\) is the tolerance, the quotient distance obtained for the longer distance between points A and D is less than that obtained for points A and C. In fact, the quotient distance between two points on any straight line, controlled by the tolerance, is not greaterthan the quotient of AB + BC to AC). The generalized formula for calculating the maximum quotient distance in a non-concave graph is represented by the following equation:  
\[\left\{\begin{array}{l}a^{2}+b^{2}=\xi^{2}\\ a,b\in N\\ a\geq b\end{array}\right. \tag{5}\]  
\[qd_{\max}=\frac{\xi+1}{\sqrt{a^{2}+\left(b+1\right)^{2}}} \tag{6}\]  
\(\xi\) denotes the tolerance; \(\xi^{2}\) needs to be the sum of the squares of two natural numbers, such as 1, 2, etc.; and \(qd_{\max}\) denotes the maximum quotient distance value. After the \(qd_{\max}\) value is determined, thereby enabling the manipulation of voting outcomes through the adjustment of voting weights associated with point pairs surpassing the \(qd_{\max}\) threshold, this procedure can effectively distinguish non-concave graphs from other topological structures.
## 5 Future Perspectives for DprE1 and MmpL3 Inhibitors in Clinical Therapy  
Promiscuous targets could be an obstacle in the discovery of novel antitubercular drug targets, being recurrently found in HTS [110], nevertheless, they have been proven really useful for the development of novel drug candidates. This is well demonstrated by the fact that five compounds currently in clinical trials inhibit these targets, with DprE1 inhibitors TBA-7379, BTZ043 and Macozinone in Phase I, while the QcrB inhibitor Telacebec, the MmpL3 inhibitor SQ109 and again the Macozinone are in Phase II [26; 27; 28; 29].  
In particular, Macozinone has completed a dose-escalation phase I study in healthy male volunteers and a multiple ascending dose study in Russia, which demonstrated the good safety profile of the compound. Moreover, in 2018 it completed a Phase IIa Early Bactericidal Activity (EBA) study in  
Figure 3: (**A**) Crystal structure of Mtb MmpL3 and detail of the inhibitors binding site (SQ109, PDB: 6A)G with carbon atoms in green; AU1235, PDB: 6 AJH with carbon atoms in magenta; ICA38 PDB: 6 AJJ with carbon atoms in cyan [25]. (**B**) Mechanism of transport of MmpL3: The flow of protons through charged residues in the transmembrane region of MmpL3 drives conformational changes that lead to the transport of TMM from the membrane to the periplasmic space. (**C**) MmpL3 inhibitors bind to a pocket in the transmembrane region of MmpL3, disrupting the electrostatic interactions between D256 and Y257 with Y646 and D645, respectively. This blocks the proton relay pathway and ultimately TMM translocation.  
Russia and Belarus in drug sensitive-TB (DS-TB) patients, confirming the safety and efficacy of the drug. In parallel, a Phase I clinical study has been initiated in Switzerland, by the nonprofit Innovative Medicines for Tuberculosis (iM4 TB) foundation [24].  
The MmpL3 inhibitor SQ109 has completed a Phase 2b study in Russia which demonstrated its efficacy in MDR-TB patients, with a good tolerability profile [111]. Furthermore, SQ109 was demonstrated to enhance the activity of isoniazid, rifampicin and bedaquiline and shortened clearance of TB in a mice model. The drug now has also completed three Phase I studies in the USA and two Phase II studies in Africa in DS-TB patients.  
These studies thus demonstrate the great potential of these targets for the development of a new drug to reach the objective to eradicate TB.  
Conceptualization, G.R. and L.R.C.; writing, G.D., J.M.B. and L.R.C., review and editing, M.R.P., E.D.R., G.R. and L.R.C.; visualization, J.M.B. and L.R.C.; project administration, G.R. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Italian Ministry of Education, University and Research (MIUR): Dipartimenti di Eccellenza Program (2018-2022)--Dept. of Biology and Biotechnology \"[PERSON]\", University of Pavia (G.D., M.R.P., E.D.R., G.R. and L.R.C.).  
The authors declare no conflict of interest.
## 5 Conclusions  
In this paper, a physical model of a solar chimney under the combined effect of wind pressure and thermal pressure is developed, and the natural ventilation performance of the solar chimney under different conditions of solar radiation intensity, heat flow density of the indoor heat source, outdoor wind speed, and the relative position of the indoor heat source is studied by three-dimensional numerical simulation. The main conclusions are as follows:  
Within the scope of this paper, as the solar radiation intensity, indoor heat source heat flow density, and outdoor wind speed increase, their effects on the ventilation performance of solar chimney buildings are enhanced. As the intensity of solar radiation increases, solar chimney ventilation gradually increases, with a maximum increase of 33.1%. But the increase in ventilation decreases with the increase in heat flow density of the indoor heat source.  
With the increase in indoor heat source heat flow density, the solar chimney ventilation volume gradually increases; the maximum value of the increase is 32.1%. The enhancement of the size of the heat source on the size of the ventilation capacity of the solar chimney  
Figure 12: Variation of ventilation volume with solar chimney inlet width.  
will be investigated at a later stage. When the indoor heat source is located in the middle of the room, it has a stronger influence on the effectiveness of the solar chimney draught compared to other orientations.  
As outdoor wind speed increases, the amount of solar chimney ventilation also increases and increases more, but the amount of ventilation increases and decreases with the increase in solar radiation intensity and heat flow density of the indoor heat source.  
Outdoor wind affects the optimum inlet width of the solar chimney, with a preference for 0.2-0.3 m as the optimum inlet width, without considering outdoor wind. However, when considering the effect of outdoor wind, the optimum inlet width is located at a relatively large 0.5 m.  
Conceptualization, S.Y. and Z.G.; methodology, S.Y., Z.G., J.X. ([PERSON]) and Z.X.; software, S.Y.; validation, S.Y. and S.Z.; formal analysis, S.Z., J.L. and J.X. ([PERSON]); investigation, S.Z., J.L. and J.X. ([PERSON]); resources, Z.X.; data curation, S.Z. and J.X. ([PERSON]); writing--original draft preparation, S.Z. and J.X. ([PERSON]); writing--review and editing, S.Y. and J.X. ([PERSON]) All authors have read and agreed to the published version of the manuscript.  
This work was supported by National Natural Science Foundation of China (Grant No. 52106017), Beijing Natural Science Foundation (Grant No. 3222031), Research Fund of Yunnan Province (Grant No. 202001 BB050070), Scientific Research Foundation of Kunming Metallurgy College (Grant No. XArccm201802).  
Not applicable.  
Data Availability Statement: Data are contained within the article.
## 2 Materials and Methods
### Numerical Simulation  
The numerical welding simulations were carried out in the MSC software Simufact Welding 2020 with two discrete parameter sets. The 4 kJ/cm single track set was welded, tested and used to create the simulation model. For validation of the simulation model and the property prediction, a parameter set with energy input per unit length 8 kJ/cm was used with a layer height of 2 mm. The numerical welding simulation was carried out for a wall structure with 300 \(\times\) 6 \(\times\) 50 mm, which is in x- and y- direction the same as the experimental samples. The lower height was a change made to achieve a lower simulation time. Due to the reduction of simulation time, the 20 th layer was chosen for the evaluation of the numerical simulation. The \(\mathrm{t_{8/5}}\) cooling time for all simulations was derived in layer 20 in order to reduce calculation time. As [PERSON] et al. stated, the quasi-static part starts in layer 20 for wall structures made of G4 Si1 [50]. In one welding layer there is more than one passthrough of the temperature field of 800 \({}^{\circ}\)C-500 \({}^{\circ}\)C. The used \(\mathrm{t_{8/5}}\) cooling time is the last passthrough with a complete austenitization, as in the experiments. The mesh convergence is demonstrated in Figure 6, the \(\mathrm{t_{8/5}}\) cooling time was chosen as a factor to compare the different mesh sizes. The curves for the three different mesh sizes, 0.5 mm, 1 mm and 2 mm follow the same trend, though shifted in time direction. This shift is  
Figure 4: Layout of samples for mechanical testing of an additively manufactured wall structure.  
Figure 5: Testing equipment for mechanical properties; **left**: universal testing machine Zwick 1455, **middle**: WPM PSd 300/150, **right**: Stravers DuraScan 70.  
due to the temperature constraint that the welding process will start dynamically when all elements have a temperature below 100 \({}^{\circ}\)C, which differs based on the number of elements. The \(\mathrm{t_{8/5}}\) cooling times were 7.867 s for 0.5 mm, 7.238 s for 1.0 mm and 7.552 s for 2.0 mm. This results in a small difference of 4% between 2.0 mm mesh size to 0.5 mm and a difference of 4% between 1 mm and 2 mm mesh size. Therefore, the mesh size of 2 mm was chosen to ensure a short simulation time. Further boundary conditions can be seen in Table 3.  
The simulated \(\mathrm{t_{8/5}}\) cooling times can be used to calculate the associated mechanical properties using the found regression equations for each mechanical property.  
With the combination of simulated \(\mathrm{t_{8/5}}\) cooling times, process parameter and calculated mechanical properties a complete set of training data is generated to be used in machine learning applications. The simulation, in combination with the regression equation, can also be used to manually predict mechanical properties of a given three-dimensional workpiece before manufacturing or testing.
# Hard Disk Failure Prediction Based on Blending Ensemble Learning
## 4 Experiment
### Data Preprocessing
#### 4.2.1 Feature Selection
The darker the cell, the stronger the correlation between the two features, and the weaker the correlation for the converse situation. As shown in Figure 6, the three features No. 240, 241, and 242 are taken as an example. They are highly correlated with each other, indicating that they represent basically the same meanings. The significance of drawing a heat map is to select one or two of the highly correlated features as the final selection, so as to avoid wasting additional resources in the training process due to feature redundancy. The specific meanings of the five S.M.A.R.T. features selected after the above processing are as follows:  
1. S.M.A.R.T. 1: Raw read error rate. This attribute refers to the number of errors that occur when the head reads data on the disk surface. It is generally 0. This attribute may be a large value for Seagate hard disks, but it is normal and is fine as long as it does not increase any more. 2. S.M.A.R.T. 7: Seek error rate. This attribute represents the number of errors generated by the head in the seek process. Generally, it is 0. There are many factors that can cause this value to rise, and they are often hardware-level reasons, including disk surface media problems and temperature anomalies. Similar to the No. 1 feature, for Seagate, this feature of the new hard disk may also be a large value, which will normally decrease in the future. 3. S.M.A.R.T. 187: Reported uncorrectable errors. A parameter exclusive to Seagate hard disks, which can be understood literally, it represents the number of errors that cannot be solved by the hardware reported to the operating system. If this value is non-zero, it means that the user needs to back up the hard disk data. 4. S.M.A.R.T. 198: Offline uncorrectable sector count. This feature is the cumulative count of uncorrectable errors in the read/write sector. The data should be 0. If this parameter is not zero and is rising, then a mechanical problem has occurred, and a sector must be damaged. If a file is working on the damaged sector, the operating system will return the disk read error message and remap the sector the next time it is written to. Figure 6: Heat map based on 15 features preliminarily selected. S.M.A.R.T. 240: Head flying hours. The parameter that increases with time, such as the power on time and the total number of reads and writes, can be approximated to the age of the hard disk and has no other special significance.
## 3 Life Cycle Assessment Methodology
### Life Cycle Impact Assessment
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Methodology** & **Midpoint Impact Categories** & **Areas of Protection** \\ \hline \multirow{7}{*}{CML} & **Obligatory impact categories:** Depletion of abiotic resources, & \\  & land competition, climate change, stratospheric ozone depletion, & Human health, \\  & human toxicity, freshwater aquatic ecotoxicity, marine aquatic & natural \\  & ecotoxicity, terrestrial ecotoxicity, photo-oxidant formation, & environment, \\  & acidification and eutrophication. & man-made \\  & **Optional impact categories:** Loss of life support function, loss of biodiversity, & environment, \\  & freshwater sediment ecotoxicity, marine sediment ecotoxicity, impacts of & human \\  & ionizing radiation, malodorous air, noise, waste heat, casualties, lethal, & resources \\  & non-lethal, depletion of biotic resources, desiccation, and malodorous water & \\ \hline \multirow{3}{*}{EDIP 2003} & Global warming, ozone depletion, acidification, terrestrial & Human health, \\  & eutrophication, aquatic eutrophication, photochemical ozone & ecosystem and \\  & formation, human toxicity, ecotoxicity, and noise & resources \\ \hline \multirow{3}{*}{TRACI} & Ozone depletion, global warming, smog formation, acidification, & Human health, \\  & eutrophication, human health cancer, human health noncancer, human health & ecosystem and \\  & criteria pollutants, ecotoxicity, and fossil fuel depletion & resources \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Midpoint-oriented LCIA methodologies [65] (CML = Centrum voor Milieukunde Leiden; EDIP = Environmental Development of Industrial Products; TRACI = Tool for the Reduction and Assessment of Chemical and Other Environmental Impacts). \begin{table}
\begin{tabular}{c c c} \hline \hline
**Methodology** & **Damage Categories (Endpoint Categories)** & **Areas of Protection** \\ \hline \multirow{3}{*}{EI99} & Climate change, ozone layer depletion, acidification, & Human health, \\  & eutrophication, carcinogenic, respiratory effects, ionizing & ecosystem and \\  & radiation, ecotoxicity, land use, mineral resources, fossil resources & resources \\ \hline \multirow{3}{*}{EPS 2000} & Life expectancy, severe morbidity and suffering, morbidity, severe nuisance, & Human health, \\  & nuisance crop production capacity, wood production capacity, fish and meat & ecosystem \\ \cline{1-1}  & production capacity, base cation capacity, production capacity for water, share & production capacity, \\ \cline{1-1}  & of species extinction, depletion of element reserves, depletion of fossil reserves (gas), depletion of fossil reserves (oil), and & biodiversity and \\ \cline{1-1}  & depletion of fossil reserves (coal), depletion of fossil reserves (oil), and & abiotic stock \\ \cline{1-1}  & depletion of mineral reserves & resources \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Endpoint-oriented LCIA methodologies [65] (EI99 = Eco-Indicator 99; EPS 2000 = Environmental Priority Strategies in Product Design 2000; JEPIX = Environmental Policy Priorities Index for Japan).
# L5 IN: Overview of an Indoor Navigation Pilot Project
## 3 Implementation
### 5G Campus Network Infrastructure  
For the implementation in the LSIN project, a 5G non-standalone (5G NSA) campus network and an additional experimental system will be realized at HafenCity University Hamburg. Applications from the field of indoor navigation will be tested there and will serve research purposes.  
The 5G NSA network will comprise an outdoor campus network (Figure 4). Since outdoor antennas cannot guarantee coverage for every room, additional antennas will be set up indoors to create an additional indoor network (Figure 5). In this way, a greater area can be covered and the influence of the varying proportions of reinforced concrete components and lightweight walls on the quality of navigation in the interior can be investigated. To investigate the different characteristics and setups, different environments will be systematically tested. While one test floor will be characterized by large lecture halls and thin walls, the second test floor will be characterized by many small rooms and thick walls.  
This network will use the private frequencies in band B43/N78 (3.7-3.8 GHz) with a 4G LTE anchor band. For the navigation of persons, sufficient accuracy is required for the positioning. The network data will be transmitted in real time, e.g., to change the navigation path, and the user behavior shall be analyzed.
# A Survey of Blockchain Applications for Management in Agriculture and Livestock Internet of Things
## 3 Challenges and Motivations
### Current Challenges in Agricultural and Livestock IoT
Additionally, the exposure of commercial information--such as production methodologies, yield figures, and marketing strategies--poses equally significant risks: breaches could result in severe financial losses and erode competitive advantages. Beyond these immediate issues, the long-term implications for small-scale farmers warrant closer scrutiny. Many smallholders lack the technical infrastructure and financial resources to adopt blockchain-enabled IoT systems, heightening the risk of exclusion from modernized supply chains. Over time, this could widen the digital divide and exacerbate economic inequalities in the sector as technologically advanced enterprises capitalize on real-time data sharing, precision monitoring, and enhanced market access. Furthermore, blockchain's decentralized nature--while it promotes transparency--can inadvertently compromise privacy, making sensitive details (e.g., farmers' personal information, land-specific data, or business trade secrets) accessible to unauthorized third parties. To mitigate these challenges and ensure responsible implementation of blockchain technology, it is vital to balance transparency with robust privacy protections. Selective disclosure and role-based access controls can shield high-value or personal data, while dedicated funding, training programs, and community-based resource centers can help smallholders overcome financial and technical barriers. Equally important are legal frameworks that safeguard personal and commercial information, along with continuous monitoring and assessment to evaluate the long-term socioeconomic impact on small-scale farmers. Through these collective efforts, blockchain-integrated IoT systems can drive efficiency and trust in agriculture and livestock management without marginalizing vulnerable stakeholders or compromising data security.