esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Water Composition Analysis  
A physicochemical analysis of four different water samples was provided: water C--redistilled water, serving as a control sample; water P--tap water taken from left-bank Warsaw (Praga-Poludnie District); water B--tap water taken from right-bank Warsaw (Bielany District); water U--tap water taken from right-bank Warsaw (Ursynow District). The selection of these samples made it possible to compare the quality of water supplied in different areas of Warsaw and assess the possible influence of its parameters on the study. Physicochemical analysis of the water was carried out following current standards and testing procedures. Parameters such as pH (using the potentiometric method) and electrical conductivity (using the conductometric method PN-EN ISO 10523:2012 [21]) were assessed. The content of ions such as nitrates (PN-EN 26777:1999 [22]), nitrites (PN-82/C-04576.08 [23]), and ammonium ions (PN-ISO 7150-1:2002 [24]) was determined using spectrophotometry. Chlorides (PN-ISO 9297:1994 [25]), water hardness (PN-ISO 6059:1999 [26]), and permanganate index were determined using titration. The ICP-OES (inductively coupled plasma with optical emission separation atomic spectrometry) method was used to assess the water's iron and manganese (PN-EN ISO 11885:2009 [27]) content. Sulfates were determined according to the ISO 15923-1:2013 [28] procedure, defining methods for automated spectrophotometric and turbidimetric analyses using a discrete analytical system.
# Integrated Photodetectors Based on Group IV and Colloidal Semiconductors: Current State of Affairs
## 3 Group IV Semiconductors
### Carbon
#### 3.3.1 Carbon Nanotube
The obtained photodetector shows a spectral FWHM of about 33 nm at a signal wavelength of 1200 nm and a higher suppression ratio until a power density of 0.07 W cm\({}^{-2}\) and photocurrent of 5 pA. Moreover, taking advantage by the theory of the \"resonance and off-resonance\" cavity, the authors realized cavity-integrated chirality-sorted CNT-film detectors for specific target signal detection, operating at zero bias and resonance-allowed mode. By integrating multiple array detectors on a chip working at different target signals, a multiwavelength signal detector system can be obtained; this device can be used in the fields of colour imaging, monitoring, signal capture, biosensing, and on-chip or space information transfers [90]. A broadband nano-photodetector based on single-layer graphene-CNT thin film (SLG-CNTF) Schottky junction was studied and developed by [PERSON] et al. [80]. SLG-CNTF device shows peak sensitivity at 600 and 920 nm, a fast response speed (see Figure 4a; \(\tau_{\text{r}}\) = 68 \(\mu\)s, \(\tau_{\text{f}}\) = 78 \(\mu\)s, much faster than several seconds response time of the graphite quantum dots/graphene heterojunction [91]) and good reproducibility for switching frequencies in the range 50-5400 Hz (the normalized photocurrent at different frequencies shown in Figure 4b). The responsivity and detectivity at different bias voltages were investigated, as shown in Figure 4c, where is evident that both parameters increase with decreasing bias voltage. Moreover, the authors studied the photocurrent at different light intensities (from 0.25 to 12 mW cm\({}^{-2}\)) finding a high dependence of the photocurrent of the SLG-CNTF Schottky junction photodetector on the intensity of incident IR. The trend of responsivity and detectivity as a function of light intensities is reported in Figure 4d where a saturation of both parameters is evident at high intensity because the carrier recombination rate is reduced at high light intensity [92]. Recently, CNT/Si photodetectors in two electrode configurations for photovoltaic and photoconductive operations were studied under nanosecond light pulse [93]. A linear dependence of the photocurrent as a function of the light pulse energy was observed in photovoltaic mode with rise time of 20 ns, while when the device operates in photoconductive mode, the maximum photocurrent increases up to 30 times with a gain in the number of photogenerated charges till 200% and a reduction in the time response below 10 ns. At carbon nanotube/Si interface a Schottky junctions is formed, leading to a fast response, as pointed out by the current-voltage characteristics measured as a function of the temperature [93]. These findings open the possibility of CNT/Si photodetectors used as avalanche photomultipliers.
# Configuration of Non-Pumping Reactive Wells Considering Minimum Well Spacing
## 3 Results and Discussion  
### Practical Configuration of NPRW  
#### 3.1.1 Removal of Contaminant Plume by Single NPRW  
To evaluate the capture width of single NPRW, the numerical case study was performed using different conditions of hydraulic conductivity and radius of the material filling in NPRW. Here the capture width was determined by measuring the width of the decontaminated water plume that appeared downstream of the well (Figure 2a). When the hydraulic conductivity of the reactive material (\(\mathrm{K_{w}}\)) was higher than that of the surrounding geologic media (\(\mathrm{K_{a}}\)), the capture width (\(\mathrm{d_{2}}\)) was wider than their radius (\(\mathrm{d_{1}}\)). The ratio of capture width over well diameter (\(\mathrm{d_{2}}\)/\(\mathrm{d_{1}}\)) increased depending on the hydraulic conductivity difference (\(\mathrm{K_{w}}\)/\(\mathrm{K_{a}}\)) (Figure 2b). At \(\mathrm{K_{w}}\)/\(\mathrm{K_{a}}\) higher than 25, the capture width, \(\mathrm{d_{2}}\)/\(\mathrm{d_{1}}\), reached a maximum 1.9. Without loss of consistency, as the diameter of a NPRW increased, the capture width also increased linearly (Figure 2c). Previous researches already reportedthe dependency of capture with of NPRW on diameter as well as hydraulic conductivity of well. They also reported that the maximum capture width (d\({}_{2}\)/d\({}_{1}\)) could not exceed 2.0 even by using highly permeable material [25, 28, 30]. In order to block a given width of contaminant plume using NPRWs, an efficient well arrangement is important.
## 2 Literature Review  
In recent years, numerous studies have proposed scheduling policies for DL training tasks within GPU clusters that leverage ML approaches. For instance, Optimus [10] predicts the number of epochs needed for model training and utilises this information to estimate the total training time on allocated resources. Similarly, the approach presented in [11] develops predictors for the training time of specific network components, such as fully connected and convolutional layers. In this case, the overall training time is the sum of individual times for each component. Furthermore, the study outlined in [12] introduces a method for predicting resource consumption based on GPU selection across various DL models, enabling the estimation of training time beforehand. [PERSON] [13] also takesinto account resource consumption by profiling job memory requirements to ensure that a job can receive sufficient memory to execute, thereby avoiding the out-of-memory (OoM) problem. On the other hand, works in [14; 15; 16] employ Reinforcement Learning (RL) to formulate scheduling policies. It is noteworthy, however, that these ML-related scheduling policies necessitate a substantial volume of data for training.  
While the variability of training performance in a heterogeneous GPU cluster has been explored in prior research, the extent of such investigations remains limited. Notably, [PERSON] [8] and [PERSON] [6] select GPU architectures for jobs based on the DL model but do not encompass other crucial factors such as the number of GPUs used for distributed training and hyperparameters (e.g., batch size). More importantly, the batch size is emphasised as one of the necessary consideration variables of a computation-related system [17].  
As the DL training process is iterative, a scheduler can derive benefits from the ability to suspend and resume at specific points (i.e., checkpointing) and reschedule the job with better resources when they become available. Various scheduling policies have been devised on this premise, such as Gandiva [9], DL2 [18], and Optimus [10]. These policies allocate resources to jobs and assess training performance dynamically to achieve satisfactory results, but, ultimately, this approach is still far from optimal.  
[PERSON] [9] and [PERSON] [8] reveal that a small model often underutilises a GPU. To address this issue, they have introduced the concept of job packing, enabling the simultaneous training of multiple models on a single GPU. However, these studies highlight that concurrent jobs can potentially interfere with each other, adversely affecting training performance. Furthermore, the extent of interference depends on the DL models themselves [19; 20]. In the pursuit of identifying suitable job combinations, [PERSON] employs a trial-and-error approach, while [PERSON] establishes a threshold for the difference between isolated training and packing decisions. In both cases, there are limitations. [PERSON] spends training time searching for better solutions, and [PERSON] uses a constant threshold that is set empirically.
## 2 Materials and Methods
### [PERSON]'s Model  
From a practical point of view, it is important to get an answer to two questions:  
1. Do the moisture values calculated by formula (8) differ from the values determined using other models, for example, [PERSON]'s model \(M_{R}=e^{-kt}\)[17]?
2. To what basis (w.b. or d.b.) do the simulation results correspond?  
The answer to the second question is especially important because, as shown above, the estimates of moisture content are almost the same only at low material moisture, but the discrepancy between the estimates quickly increases with increasing moisture content (Figure 1).  
Regarding the choice of [PERSON]'s model, which is also called the [PERSON] model, we note that this model is the simplest and is often used by researchers [1; 16].  
To get answers to the questions formulated above in an analytical form, we will perform the following transformations. We transform Equation (8) to the form (19):  
\[C_{water}^{(d.b.)}=\frac{C_{water}^{(w.b.)}}{1-C_{water}^{(w.b.)}}=\frac{1}{ \left(C_{water,start}^{(w.b.)}\right)^{-1}-1} \tag{19}\]  
Substitute (18a) into (19). We get after the transformations:  
\[C_{water}^{(d.b.)}=\frac{C_{water,start}^{(w.b.)}e^{-\theta}}{1-C_{water,start}^ {(w.b.)}}=\frac{MC_{water,start}^{(w.b.)}e^{-\theta}}{M\left(1-C_{water,start}^ {(w.b.)}\right)} \tag{20}\]  
Here \(MC_{water,start}^{(w.b.)}=M_{water,start}\) (which follows from (2)). In addition, taking into account Equations (2), (3) and (1), we write: \(M\left(1-C_{water,start}^{(w.b.)}\right)=M-M_{water,start}=M_{dry,start}\). Then, taking into account Equation (4), we get:  
\[C_{water}^{(d.b.)}=\frac{M_{water,start}}{M_{dry,start}}e^{-\theta}=C_{water,start }^{(d.b.)}e^{-\theta}. \tag{21}\]Taking into account relation (12), we come to the conclusion that model (21) coincides with [PERSON]'s model [19], which is often written in normalized form [14]:  
\[\frac{C_{water}^{(d.b.)}}{C_{water,start}^{(d.b.)}}=e^{-kt}. \tag{22}\]  
Here \(k=\tau^{-1}\). The value \(\tau=k^{-1}\) can be determined using known techniques [17; 19].  
Normalized moisture (22) does not directly depend on the initial moisture (but the initial water content taken into account indirectly by the coefficient \(k\)).  
Summarizing, we state that relations (21) and (22) are equivalent to [PERSON]'s model and determine the moisture content on a dry basis (d.b.). Models (18) and (18a) determine the moisture content on a wet basis (w.b.). In addition, it should be noted that relations (18) and (18a) explicitly take into account the effect of initial moisture content, which is especially important in predicting the drying time of materials with high initial moisture content. As noted above, explicit consideration of the influence of the initial moisture content of the material, presumably, expands the predictive capabilities of the model. The validity of this assumption is confirmed in the following presentation of the material of the study.
## 5 Conclusions
Taking advantage of the high range and temporal resolution measurements, new particle formation and near real-time aerosol dynamics of such processes could be addressed in the future. This will give us the possibility to explore new light-scattering problems related to lidar technologies. Conceptualization, R.C. ; methodology, R.C., A.E.B.-V., V.M. ; software, R.C., A.E.B.-V., V.M. ; validation, R.C., A.E.B.-V., V.M. ; formal analysis, R.C., A.E.B.-V., V.M. ; investigation, R.C., A.E.B.-V., V.M. ; resources, R.C. ; writing, R.C., A.E.B.-V.; visualization, R.C., A.E.B.-V.; supervision, project administration and funding acquisition, R.C. All authors have read and agreed to the published version of the manuscript. This work was founded by ONERA, The French Aerospace Lab, within the framework of PROMETE Project. The authors would like to thank [PERSON] (RDDC-DRDC, Canada) for his valuable advices in the field of lidar design and inversion. The authors are grateful for helpful discussions with [PERSON] (ONERA, France), [PERSON] (ONERA, France), [PERSON] (ONERA, France), and [PERSON] (Kansas State University, USA). We thank [PERSON] and [PERSON] from Leukos ([[https://www.leukos-laser.com/](https://www.leukos-laser.com/)]([https://www.leukos-laser.com/](https://www.leukos-laser.com/))) for developing and proving the laser source. The authors declare no conflict of interest. The following abbreviations are used in this manuscript:  
ADC Analog-to-digital converter  
APD Avalanche Photo-Diode  
BC Background noise current  
BRDF Bidirectional Reflectance Distribution Function  
DC Dark noise current  
DHR Directional-Hemispherical Reflectance  
DSP Digital Signal Processing  
LIDAR LIght Detection and Ranging  
LR Lidar ratio  
NBF Neutral band filter  
Nd:YAG Neodymium-doped Yttrium Aluminum Garnet  
ONERA Office National d'Etudes et de Recherches Aerospatiales  
PTFE Polytetrafluoroethylene  
RCS Range-Corrected-Signal
###### Abstract  
In recent years, short-reach optical links have attracted much more attention and have come to constitute a key market segment due to the rapid development of data-center applications and cloud services. Four-level pulse amplitude modulation (PAM4) is a promising modulation format to provide both a high data rate and relatively low cost for short-reach optical links. However, the direct detector and low-cost components also pose immense challenges, which are unforeseen in coherent transmission. To compensate for the impairments and to truly meet data rate requirements in a cost-effective manner, various digital signal processing (DSP) technologies have been proposed and investigated for short-reach PAM4 optical links. In this paper, an overview of the latest progress on DSP equalization technologies is provided for short-reach optical links based on PAM4 modulation. We not only introduce the configuration and challenges of the transmission system, but also cover the principles and performance of different equalizers and some improved methods. Moreover, machine learning algorithms are discussed as well to mitigate the nonlinear distortion for next-generation short-reach PAM4 links. Finally, a summary of various equalization technologies is illustrated and our perspective for the future trend is given.  
short-reach optical links; direct detection; four-level pulse amplitude modulation; digital signal processing; equalization +
Footnote †: journal: Applied Sciences
## 1 Introduction  
Titanium (Ti) and Ti-based alloys generally present desirable mechanical characteristics, corrosion resistance, and biocompatibility [1, 2, 3]. Titanium-based alloys have been extensively used in the last four decades to create implant elements and medical tools like prostheses or dental implants. The corrosion resistance and, at a certain level, antibacterial properties of titanium-based alloys in biomedical applications are mainly due to the formation of a passive TiO\({}_{2}\) layer on the surface [4, 5]. The domain of oral implants involves many medical fields, with breakthroughs made in surgery, prosthetics, and dental prosthetics, in particular, offering new possibilities in the restoration of prosthetics [6, 7]. There are few published experimental results about the chemical, electrochemical, terminal, or combined features of surface preparation of Ti-based alloys that facilitate both the desired chemical, mechanical, and biological reactions at a superficial level and the deposition of nonmetallic layers [8, 9, 10, 11, 12]. TiAlZr systems have gained some attention as implantable materials in recent years and are considered a potential solution for medical applications where Ti is not applicable [3, 4, 5, 13, 14]. Hydroxapatite (HA) in large or small quantities is not toxic to biological cells) and is almost inactive in biological environments. Additionally, numerous studies have investigated and confirmed the excellent properties of hydroxyapatite in contact with bone tissues [15, 16]. The most efficient method of enhancing the osteogenesis of Ti-based alloys in medical implants is covering them with a bioactive layer such as hydroxyapatite. Electrophoresis deposition is a technique that uses the motion of charged particles in a suspension in the presence of an electric field. This electric field allows the formation of well-established particles in thin layers, modified in layers regardless of their shape, or even in thicker layers on their own. The first applications of electrophoresis were in the modeling of ceramic materials and in the production of coatings. There is also a growing interest in the use of this technique in the handling of biomaterials and biological components such as natural polymers, proteins, bacteria, and cells. The process is useful for the application of polymeric materials, pigments, dyes, ceramics, or metals on any surface of an electrically conductive material. The microstructural, chemical, and mechanical characteristics of complex elements obtained via superficial layer deposition remain to be determined [17; 18; 19].  
The paper deals with a chemical treatment method for the surface of an implantable metallic material based on Ti, deposition of a thin layer of hydroxyapatite (HA) through electrophoresis, and analysis of the resistance to electrocorrosion of the resulting films using EIS. Surface investigations were performed via 2D and 3D SEM.
## 3 Methods
### The Decision Engine  
The content based anomaly detection engine clusters and ranks the topics as described earlier. The decision engine, based on a certain threshold value set by the operator (in our case \(m=2/3\), reason discussed later) divides the topics into two fractions, the first fraction consisting of two-thirds of the highest ranked topics that we assume to be normal and the second fraction consisting of the rest of the topics that are initially assumed to be potentially anomalous. Each topic is represented by a tuple consisting of its top ten words. Each anomalous topic is compared with each of the normal topic in the following manner:  
1. We find the semantic distance between the first word in an anomalous topic and the first word in one of the normal topics. We aggregate it over all words in the given normal topic and aggregate that over all the words in the given anomalous topic. Let \(S^{m}(i,j)\) denote the similarity between the \(i^{th}\) word in a test topic and \(j^{th}\) word in the \(m^{th}\) typical topic. \(I\) and \(J\) stand for the total number of words in a test topic and a typical topic respectively (10 in our case). Then relatedness of the test topic with one typical topic is measured as follows. The semantic distances used by us were the ones based on the WordNet and Normalized Google Distance as described above. \[r=\sum_{j=1}^{J}\sum_{i=1}^{I}S(i,j)\] (8)
2. We then aggregate that over all the normal topics, which finally gives us a measure of similarity of the given anomalous topic with all the normal topics. \(M\) stands for the total number of typical topics. Hence, relatedness between the given \(p^{th}\) test topic and all the typical topics is measured as follows: \[r_{total}=\sum_{m=1}^{M}\sum_{j=1}^{J}\sum_{i=1}^{I}S^{m}(i,j)\] (9)
3. We repeat the two steps above for all potentially anomalous topics.  
Based on the newly available contextual information, the anomalous topics are sorted in ascending order of the context score obtained. Topics with the \(k\) lowest scores are considered anomalous. In our current setup, \(m\) is determined via user input, since we are modeling anomaly detection in unsupervised settings. This technique can easily be adapted to supervised settings, where a white list of allowable or typical topics informs our judgment of the \(m\) threshold in the decision engine. It is also possible to adaptively use analyst input to change these parameters to refine the accuracy of our system during operation. For example, based on past statistics or domain knowledge, if the operator decides that a certain kind of topic should not be considered anomalous, then he can incorporate it in the list of normal topics, so that topics with scores close to this one are no longer considered anomalous. Similarly, if the operator decides that a certain topic should always be considered anomalous, the threshold \(m\) can be revised upwards.