esa-sceva/satcom-chunk-collection huggingface dataset:
# Innovative Approaches for Minimizing Disinfection
## 4 DBP Minimization Approaches  
### DBP Precursors Removal  
Numerous methods for removing NOM in drinking water treatment have been investigated, including coagulation and flocculation, sedimentation (Figure 5), activated carbon adsorption, advanced oxidation processes, and membrane-based approaches [21]. Conventional drinking water treatment processes like coagulation and filtration serve as effective pretreatment steps before disinfection, helping to lower DBP formation by reducing the concentration of NOM [1; 29].  
Figure 5: Sedimentation tank effluent.  
Following their formation, methods such as coagulation/flocculation, adsorption, biological filtration, aquifer storage and recovery, ozonation/ultrafiltration, AOPs, or hybrid treatment processes are commonly used for removing THMs from drinking water post-disinfection.  
Various methods that are aligned with biological or chemical technologies have been developed for the reduction/removal of THM levels from water, with an emphasis on the concepts of preventive action, through non-chlorine disinfectants or optimization of the chlorine dose, and treatment action that removes precursor substances with ion exchange resin or membrane filtration before chlorine addition. Following their formation, methods such as coagulation/flocculation, adsorption, biological filtration, aquifer storage and recovery, ozonation/ultrafiltration, AOPs, or hybrid treatment processes are commonly used for removing THMs from drinking water post-disinfection [30].  
Innovative technologies like AOPs are being closely examined. They are becoming increasingly attractive as they can mineralize NOM instead of just capturing and transferring it to another phase. Additionally, AOPs may not necessitate significant chemical doses or pH adjustments and do not produce sludge [22].  
Water treatment plants are exploring new and cost-effective advanced treatment techniques to decrease DBPs to meet regulatory requirements and tackle the challenges encountered by municipal water systems. In recent years, the prominent technologies for removing NOM from water have included enhanced coagulation (EC), electrochemical methods, advanced oxidation methods (AOM), membranes, and adsorption [10].  
Recently, heightened focus has been placed on water treatment processes, driven by growing public health and safety concerns. Various countries have implemented stringent regulations concerning both water quality and its sources. These regulations aim to guarantee drinking water's safety by eliminating hazardous substances or minimizing their concentrations to the lowest possible levels. Employing disinfection alternatives can enhance the reduction in DBPs formation [11].
## 3 Components of the Predictive Formula
### Temperature Rise Caused by Hydration  
Temperature rise caused by cement hydration is often expressed by an adiabatic temperature rise formula, which is commonly used in exponential, composite exponential, and hyperbolic forms, only considering the age of concrete. In fact, the rate of the hydration reaction is also related to the concrete temperature and its own cumulative hydration degree [31; 32; 33]. Moreover, the values tested in the field and in a laboratory are different. To reflect reality, the rate of hydration temperature rise in each period was determined via in situ monitoring. The formula of hydration temperature rise is represented in Equation (2), and the least square fitting method was used to calculate the parameters. The hydration temperature rise in each period was determined by combining the measured data and Equation (1).  
\[\Delta\mathrm{T_{c}}=\mathrm{m_{1}}\times\mathrm{exp}[-\mathrm{\tau^{m_{2}}}] \times\mathrm{\tau^{m_{3}}}\times\mathrm{T_{c}^{m_{4}}}\times\mathrm{\Delta t}, \tag{2}\]  
where \(\mathrm{\Delta T_{c}}\) is the hydration temperature rise during the time of \(\mathrm{\Delta t\,(^{\circ}C)}\); \(\mathrm{T_{c}}\) is the temperature of concrete at the beginning of the time \(\mathrm{\Delta t\,(^{\circ}C)}\); \(\mathrm{\tau}\) is the age of concrete at the beginning of the time \(\mathrm{\Delta t\,(day)}\); and \(\mathrm{m_{1}},\mathrm{m_{2}},\mathrm{m_{3}}\), and \(\mathrm{m_{4}}\) are parameters to be determined.  
Figure 4: Data regarding all the thermometers and main pipes.  
Data regarding 14 concrete blocks were selected from each of the two dams, 10 of which were used for fitting and the remaining 4 were used to verify the accuracy of the prediction method. The parameter values were obtained and are listed in Table 4. Comparisons between fitted results and measured values for the Baihetan dam and the Wudongde dam are drawn in Figures 5 and 6, respectively. The fitted and measured values generally agreed well with each other, but larger deviations were observed at the beginning when the pouring process was generally unfinished or had just been completed.
# Performance Tuning for Power Electronic Interfaces Under VSG Control
###### Abstract  
Renewable generation, such as solar PV and wind power, is commonly integrated into the power grid through inertialess power electronic interfaces (PEIs). Due to the increasing penetration of renewable generation, the frequency stability of the current power system deteriorates. In order to sustain the desired level of the overall inertia, the virtual synchronous generator (VSG) algorithm has been proposed. The concept of VSG is to enable the PEIs to emulate the external properties of traditional synchronous generators (SGs), such as inertia and primary frequency responses. By exploitation of the well-established knowledge system of conventional SG-based power grids, the VSG can also be implemented with the capabilities of primary, secondary, and tertiary frequency control in multiple temporal stages. This paper focuses on parameter tuning for VSG-PEIs by performance indices. The emulation strategies are completed with the capability of secondary and tertiary frequency regulation. The transfer functions of the dynamic systems of PEIs are simplified and referred to the control theory. The composite influences of different parameters on performance indices are analyzed. The methods of the parameter tuning are proposed according to the temporal sequences of the control stages. By typical performance standards, the proposed method is verified through simulation.  
virtual synchronous generator; frequency control; parameter tuning +
Footnote †: journal: Applied Sciences
## 1 Introduction  
The demand for anterior cruciate ligament (ACL) allografts (knee ligament replacements sourced from different patients) has changed in the last few years [1], and the postoperative results are promising. Its increased availability made allograft tissue an appealing alternative to autograft (where the ligament to replace the damaged ligament is sourced from the same patient's body) for primary ACL reconstruction, especially for ACL revision [2]. The use of musculoskeletal graft allografts has grown continuously in recent years. The American Academy of Orthopaedic Surgeons estimated that nowadays, approximately 60,000 allografts are used in knee reconstruction procedures [3,4]. However, 27% of primary ACL reconstruction operations and 57% of ACL revisions used allografts [5].  
The tendons are usually stored as deep-frozen (\(-80\)\({}^{\circ}\)C). In practice, there is no significant change in the mechanical properties of the tendons, but this has not been systematically investigated in the literature. Freezing as a method of sterilization is not tested due to storage temperature. Allografts should be sterilized before implantation as they may carry bacterial or viral diseases. [1,6,7]. In most cases, gamma radiation is used for sterilization. A radio-protectant solution is used to avoid side effects and protect tissues. A lower dose of gamma radiation (10-15 kGy) is bactericidal, whereas higher doses (30-50 kGy) are antiviral [7, 8, 9, 10, 11].  
In our preliminary study [12], we showed that the effect of radiation on the mechanical properties of tendons depends on the type of tendon. The aim of this study was biomechanical evaluation of the changes caused by freezing and gamma irradiation in a higher sample size in the mechanical properties in four different types of tendon allografts used in knee ligamentous reconstruction In this study, doubled semitendinosus and gracilis (STG), doubled tibialis anterior (TA), doubled peroneus longus (PL), and quadricep tendons were prepared and tested. The mechanical properties of the tendons, such as tensile modulus, maximum load, strain at maximum force and fracture strain were determined. We hypothesized that the type of tendon influences the effect of freezing and gamma irradiation on mechanical properties. The results of the present research confirm the results of our preliminary study [12], and can help to select the most appropriate tendons for ACL reconstruction.
###### Abstract  
For handling the broken-down communication infrastructure when a disaster event happens, this paper proposes to dispatch the unmanned aerial vehicle (UAV) to the disaster area as the relay node, which further forms a Flying Ad hoc Network (FANET). Since the UAV only owns limited energy and a disaster event may need multiple UAVs to cover its area, an efficient multi-UAV dispatch algorithm is critical to recover the communication link of the disaster area. In this paper, we adopt the mobile ground control station (GCS) to transport UAVs to the boundary of the disaster area first. According to the UAV energy consumption rate during flight and two communication modes, the UAV charging progress, and the number of required UAVs of the event, the mobile GCS then executes the proposed energy-aware multi-UAV dispatch algorithm (EAMUD) to dispatch multiple UAVs to this disaster area for building the FANET. Hence, the broken-down link in the disaster area is recovered after the FANET connects to nearby network infrastructure. Further, we propose the multi-UAV handoff scheme and exception handling processes to replace energy-exhausted UAVs for maximizing the event communication time of the disaster event. Finally, we execute simulations for related work and four EAMUD variants under different parameter values in the real scenario. These results exhibit that EAMUD with the Postpone method (EAMUD-P) achieves the highest event communication time among all these schemes.  
unmanned aerial vehicle (UAV); mobile ground control station; energy-aware multi-UAV dispatch and handoff algorithm; VANET; FANET +
Footnote †: journal: Applied Sciences  
1
## 1 Introduction
However, due to the different contributions of different feature maps or even different regions in the same feature map to the object, the features obtained by the multi-level feature networks are sweeping and multifarious, which cannot meet the requirements of the task accurately. Consequently, it is necessary to screen the information extracted from the multi-level network, and improve the performance of the instance segmentation method by biasing the allocation of usable computational resources to the most informative components [16]. An attention mechanism has been successfully applied to many computer vision tasks, such as object recognition and pose estimation, because it can assist the network to choose efficient features pertinently and enhance the learning ability of the network [17]. Furthermore, the rapid development of the attention mechanism also shows that the attention module makes the model pay more attention to the region of the image related to the object, filters out the feature map that interferes with the task, and helps the subsequent neural network precisely select effective features through learning [18; 19]. Consequently, the combination of the attention mechanism and the multi-level network can be conducive to the instance segmentation method to extract efficacious features related to the object. In this paper, the distinctive structure of an attention-based feature pyramid module (AFPM) is proposed for instance segmentation. The AFPM combining the attention mechanism and branches used to enhance location information based on feature pyramid networks (FPN) [15] is composed of feature extraction, lateral attention connections and feature enhancement. Specifically, we apply a convolutional block attention module (CBAM) [20] in bottom-up feature extraction architecture to increase the attention of a multi-level network to instance-related features. Then, a convolutional triple attention module (CTAM) [21] is included in lateral connections to filter the redundant information in network by capturing the interaction of cross-dimension between the spatial and channel dimension. Finally, we exploit the branches of strengthening spatial information to improve entire feature hierarchy; for instance, segmentation without additional parameters. The experimental results show that the proposed module can significantly boost the performance of instance segmentation on the Cityscapes dataset.
## 4 Discussion
In our work, the recombinant FtsZ protein was eluted by a solution supplemented with 500 mM imidazole, with a higher yield of 25 \(\mathrm{\SIUnitSymbolMicro g}\)/mL and purity of \(>\)90% (Figure 2D); this result was different from those in previous studies, in which higher protein yield and purity were achieved with 300-350 mM imidazole [50; 51]. The purified FtsZ protein from _E. coli_ exhibited GTPase activity at a concentration of 3 \(\mathrm{\SIUnitSymbolMicro M}\), converting approximately 13 nmol of GTP to GDP per mg of FtsZ per min (Figure 3A), lower than the FtsZ GTPase activity from _B. subtilis_, which may be related to FtsZ from different bacteria [52]. [PERSON] et al. also found that plumbagin inhibited GTPase activity and FtsZ assembly from _B. subtilis in vitro_; however, plumbagin did not affect GTPase activity and the assembly of FtsZ from _E. coli_. The data indicated that FtsZ protein from different bacteria may have different binding properties of ligand compounds [52]. It has been demonstrated that berberine can bind to FtsZ, inhibit GTPase activity, and disrupt FtsZ assembly or polymerization _in vitro_[20]. In our study, some alkaloids such as sophoridine and berberine hydrochloride were initially screened by an established screening model based on the recombinant FtsZ protein (Figure 3B). These alkaloid products exhibited high antibacterial activity against pathogenic _E. coli_, _S. enteritidis_, and _S. aureus_ strains, with MICs and MBCs of 5.12-10.24 mg/mL (Table 1). Meanwhile, several alkaloids also inhibited GTPase activity and FtsZ assembly and bound to _E. coli_ FtsZ protein (Figure 4B). The data suggested that an efficient screening system for antibacterial agents was successfully constructed based on GTPase activity of the recombinant FtsZ protein produced in _E. coli_. Furthermore, berberine hydrochloride was predicted to bind to the Asp253 residue of FtsZ protein from _E. coli_ by hydrogen bonding interaction (Figure 4C,D). Comparably, in the previous study, berberine could bind to more active sites (including Thr132, Pro134, Phe135, Ile163, Pro164, Phe182, and Leu189) of FtsZ from _Methanococcus jannaschii_ by hydrophobic interactions; this discrepancy can be attributed to: (i) different chemical structures of the ligands berberine and berberine hydrochloride and (ii) low sequence homology (<50%) and different structures of FtsZ from _E. coli_ and _M. jannaschiu_[8; 20]. FtsZ is highly divergent across bacterial species; FtsZ from _E. coli_ shares approximately 46% and 53% amino acid identity with those from _M. tuberculosis_ and _B. subtilis_, respectively [53; 54]. Therefore, the same compound may interact with FtsZ proteins from widely divergent bacterial species by different modes of action.
# Experimenting the Automatic Recognition of Non-Conventionalized Units in Sign Language
## 2 Materials and Methods
### Proposal for a Generalizable and Compact Continuous Sign Language Recognition Framework
#### 2.3.2 Learning Model  
With a signer representation \(x=\mathcal{R}(X)\), setting up a learning and prediction model consists in defining \(\mathcal{M}\) such that \(\mathcal{M}(x)=\widehat{Y}_{\text{CSLR}}\) and \(\widehat{Y}_{\text{CSLR}}\simeq Y\).  
In Section 1.2.2, we present different types of learning frameworks taking one-dimensional time-series as input, which is our case. The most effective architectures used HMMs, CRFs and RNNs. In our experiments, we chose to use RNNs, mainly for the following reasons: they are good to build complex features from not always meaningful input, they are very modular and straightforward to train and they exhibit the best results in the field of Gesture Recognition (GR) and SLR. We used LSTMs units [65] that include a cell state \(c\) in addition to the usual hidden state \(h\) of RNNs. When real-time predictions are not needed, forward and backward LSTM units can be paired to form Bidirectional LSTMs (BLSTMs). Several layers of BLSTMs can then be stacked, as shown on Figure 8 detailing a two-layer BLSTM. Our experiments usually include one to four BLSTM layers.  
An interesting addition for helping the network build relevant features is to set up the first layer as a one-dimensional temporal convolution. Temporal convolutions can help with noisy high-frequency data like ours, and are good to learn temporal dependencies [66]. A convolution layer with a kernel width of three frames is included on Figure 8.  
Using the Keras library [52] on top of TensorFlow [53], we then built a modular architecture ([[https://github.com/vbelissen/cslr_limsi/](https://github.com/vbelissen/cslr_limsi/)]([https://github.com/vbelissen/cslr_limsi/](https://github.com/vbelissen/cslr_limsi/))) with a convolutional layer (with as parameters the number of filters and the kernel size), one or multiple LSTM or BLSTM layers (with as parameters the number of units), a FC layer and a final softmax operation for classification. Finally, the training phase is associated to many parameters as well, including the learning rate and optimizer, batch size, sequence length, dropout rate, data imbalance correction and the choice of metric.  
In the next section, we aim to validate this proposal.
## 2 Methods
### Coincident Surface Observations of BGA  
Twenty-seven established and new algorithms for BGA/PC estimation, either directly or via Chl-a proxy, were tested against the BGA/PC YSI probe (sonde) data for the real CASI aircraft hyperspectral imagery and for synthetic versions of several multispectral satellites. As stated above, our goal is the regional to local operational use of decades of previous work by others on reflectance algorithms for HAB detection. For this reason, we have focused our coincident surface observations on YSI BGA/PC relative fluorescence unit (BGA_PC_RFU) values to provide water quality managers with a rapid, affordable and common method for surface BGA/PC calibration for the most promising reflectance algorithms discussed below. This approach is adequate for our task because even BGA/PC concentrations (as opposed to BGA_PC_RFU values) are not a direct indicator of toxicity. Determination of toxicity requires at least limited and focused water sampling followed by laboratory analyses [5].  
Given the variety of band center wavelengths and bandwidths for these different real and synthetic imagers, 184 variants of the initial 27 algorithm/imager combinations were created for this companion study in which we used the same aircraft imaging spectrometer dataset and field methods described in [PERSON] et al. [6]. We then evaluated the initial set of Chl-a algorithms [6], several published BGA/PC algorithms as well as some new BGA/PC algorithms and their observed image-derived indices against (field/measured/observed) BGA estimates from in-situ fluorometry using a YSI brand BGA probe for measuring PC in the field and laboratory microscope-based BGA cell counts from water samples collected within 1 h of aircraft hyperspectral imager (imaging spectrometer) acquisition.
## 3 Finite Element Simulation Study
### Finite Element Model Validation
#### 3.2.2 Structural Sound Radiation  
In order to verify the accuracy of the finite element analysis method proposed in this paper, a comparative analysis is conducted between the structural sound radiation calculation results obtained from finite element simulation and the experimental measurement data, as shown in Figure 9.  
As shown in Figure 9, within the 150-1000 Hz frequency range, two prominent peak frequencies are located at 325 Hz and 738 Hz, with relative deviations of approximately 2.17% and 2.11% from the experimental results, respectively, falling within a reasonable range. Across different frequency ranges, the discrepancies in sound power levels between simulation and experiment manifest as larger simulated peaks and smaller non-peak values. This phenomenon arises from the unevenness introduced by manufacturing processes, which is reflected in the experiments. It leads to structural variations and increased internal damping, simultaneously amplifying vibration energy dissipation and suppressing peaks. Additionally, it results in noticeable vibrations and sound radiation at frequencies beyond the theoretically predicted values. This substantiates the effectiveness of the finite element model and paves the way for subsequent finite element acoustic simulation studies.