esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Synthetic Musk Fragrances in the Mainstream of the Han River  
Table 3 also shows the concentrations of the nine SMFs (n = 153) in five mainstream sites of the Han River (H1-H5) and 17 tributaries (T1-T17), which were measured every month from March to November 2018. Various pollutants are released into the river as it passes through the densely populated area of Seoul. Following treatment in an STP, pollutants can flow into the mainstream and streams of the Han River in the form of STP effluents. In this study, five SMFs (HHCB, MK, AHTN, DPMI, and ATII) were detected in 17 tributaries; the order of both detection frequency and concentration was HHCB, MK, AHTN, DPMI, and ATII. At all sites, HHCB was detected in samples, but the concentration at each site varied from 0.017 to 2.825 \(\upmu\)g/L, with a median concentration of 0.220 \(\upmu\)g/L (Table 3). As with the STP effluents, ADBI, AHMI, MA, and MX were not detected in the Han River samples.  
The median concentrations observed in the Han River were 0.105, 0.016, and 0.010 \(\upmu\)g/L for HHCB, MK, and AHTN, respectively, indicating that HHCB was also the predominant SMF in river samples. The HHCB concentrations were higher, but the levels of AHTN were similar or lower than those reported in China [39], Singapore [40], and rivers in the Hesse region of Germany [41]. The distribution pattern of SMFs in the mainstream of the Han River was similar to that in the STP influents and effluents (Figure 2). Interestingly, the similar HHCB concentration in the STP effluents compared to those reported in STPs elsewhere indicates that HHCB might be decomposed to a relatively greater extent than in other STPs (Figure 2). Figure 3 shows the HHCB concentrations in samples taken from the STP influents, effluents, tributaries, and mainstream of the Han River. As shown in Figure 3, the SMF concentrations, especially that of HHCB, were significantly decreased in the STP effluents compared to the influents, and there was then a gradual decrease from the tributaries to the mainstream of the Han River. This implied that effective SMF removal occurred during the sewage treatment process, which together with dilution in the river represented the two main removal mechanisms of HHCB.  
Figure 2: Compositions of synthetic musk fragrances in the STP influent, effluent, tributary, and the Han River.
# Hailstorm Detection by Satellite Microwave Radiometers
## 2 Data Selection
In particular, hail with d < 10 cm (median value: 4.5 cm) strongly depress the quasi-window channels at 150 and 190 GHz where both signals converge around 175 K. These frequencies reveal a high signal dynamic also by fitting hail d \(\geq\) 10 cm where TBs are 50 K lower than in the previous case. Moreover, by comparing the individual response of these channels, it can be said that the difference (TB150-TB190) oscillates around zero when hail is captured and seems to increase with increasing hail diameter. The scattering by hail with d \(\geq\) 10 cm strongly affects measurements at 90 GHz where the signal displacement of 70 K specifies the high sensitivity of that frequency to very large hail. This is probably due to the larger wavelength allowing to better identify large particles with respect to higher frequencies that are more sensitive to the smaller aggregates. The TB differences between the two classes observed at 184 and 186 GHz demonstrate their sensitivity to the presence of hail showing that a change hail size rapidly decreases the TBs. However, since their signal dynamics are closely linked to the water vapor absorption in the middle-upper atmosphere the detection of hail based on these frequencies alone could be problematic with respect to using only window channels. We can thus state that all MHS frequencies sense the presence of hail, with the physical concept that the sensitivity to the scattering from ice increases with frequency. It needs to be specified that the interaction between hail clouds and the upwelling radiation is complex and strongly non-linear. In fact, the radiation measured by satellite is generally extinguished by a large variety of particles coexisting in the frozen region of the clouds. As demonstrated by several authors [37; 38; 39; 40], the radiation scattered by hail is not unique but associated with other hydrometeors differing in shape, density and size distribution and sometime with the presence of supercooled water above the ice layer, and therefore not easily discriminated from the ice contribution. This justifies the adoption of two wide bins of hail diameters for exploring the TB variations and it can explain the large spread of the data \begin{table}
\begin{tabular}{c c c c c c} \hline
**TB90 (K)** & **TB150 (K)** & **TB184 (K)** & **TB186 (K)** & **TB190 (K)** & **Hail Diameter (cm)** \\ \hline
199,92 & 174,57 & 202,94 & 186,31 & 175,61 & 2 \(<\) d \(<\) 10 \\ _(20,92)_ & _(17.04)_ & _(12.81)_ & _(15.35)_ & _(15.51)_ & 2 \(<\) d \(<\) 10 \\
130,10 & 121,37 & 148,94 & 134,22 & 124,38 & d \(>\) 10 \\ _(12.32)_ & _(9.47)_ & _(17.06)_ & _(14.65)_ & _(11.40)_ & d \(>\) 10 \\ \hline \end{tabular}
\end{table} 
Table 2: Averaged minimum brightness temperature (TB) thresholds at the channel frequencies calculated for the two hail diameter classes. Italics indicate the standard deviation. observed in particular in the first bin where the heterogeneity of particle sizes is higher than in the second (the standard deviations decrease correspondingly).
## 2 Materials and Methods
### Identification of the Main Computational Block  
The machine learning algorithms tested were logistic regression, random forest, and support vector machine. The labeling of the data was retrospectively possible on the whole time series, and therefore the performance of the different algorithms could be exactly numerically evaluated. As this is a multi-variate classification problem, a specific approach was required: a 10-fold cross validation protocol was applied on a balanced (composed of an equal share of all the 5 classes) and randomized dataset, with a dimension of 19,550 daily records spanning from 1 January 2020 to 1 December 2021. The number of correct predictions for each class was computed, cumulating the results of the ten-fold iterations, and allowing selection of the most performant algorithm.
# Progressive Pruning of Light Dehaze Networks for Static Scenes
## 3 Methods
### Localized Fine-Tuning for Static Viewpoint Imagery  
Building upon the robust foundation established through pretraining, we next focus on adapting our model to the specific characteristics of a fixed camera viewpoint. This localized fine-tuning process is crucial for optimizing performance in real-world deployment scenarios where the camera position remains constant. Our approach to data collection for this phase is methodical and comprehensive. We gather an extensive set of images from the fixed camera over an extended period, ensuring capture of the scene under various weather conditions, times of day, and seasons. This temporal diversity is key to developing a model that can effectively dehaze the specific viewpoint under any circumstance. The fine-tuning strategy we employ is carefully crafted to leverage the pretrained weights while allowing for adaptation to the specific scene. We implement a gradual unfreezing technique, starting with the final layers of the network and progressively allowing earlier layers to adapt. This approach helps maintain the general features learned during pretraining while fine-tuningthe model to the nuances of the static viewpoint. To prevent overfitting to the specific scene, which could potentially degrade performance under novel conditions, we employ a suite of regularization techniques. These include dropout layers, L2 regularization, and a novel adaptive weight decay method that we developed specifically for this task. Additionally, we introduce a small amount of synthetic data augmentation to the fine-tuning process, simulating variations in haze density and lighting that might not be present in our collected dataset. The performance improvements achieved through this localized fine-tuning are substantial. We observe significant increases in both quantitative metrics (PSNR and SSIM) and qualitative assessments when compared to the pretrained model. Visual comparisons reveal the fine-tuned model's superior ability to preserve scene-specific details and handle the unique lighting and atmospheric conditions of the static viewpoint.
# A Study of 5G Edge-Central Core Network Split Options
## 3 Edge-Central Split Models
### Autonomous Edge Split Option  
The autonomous edge node deployment model presumes that the edge network is a complete network including all the specific network functions. It could potentially act as a local network, providing connectivity for the devices in the specific area without having the need of a backhaul. To communicate with outside entities, a data network may be deployed immediately from the backend of the autonomous edge. Through it, the data traffic can be forwarded as IP data traffic to the network. Furthermore, there are no options to support a seamless handover to a central network or to another edge as all the dynamic subscriber information is locally maintained.  
However, the scope of such network is limited to the local area. It is not able to provide any sort of continuous mobility for the devices. To continue providing connectivity for same devices that are also in other autonomous edge networks, a new type of subscriber profile synchronization must be considered which is not in the scope of 3 GPP standardization. The local user data repository is synchronized with a central one for the target devices using database synchronization mechanisms. This can be done at a prior moment to the deployment or during the deployment time.  
Such a split alternative is most appropriate for deployments in which the local communication is most relevant, backhaul availability or capacity are highly limited and the trust in the edge infrastructure is highly increased. This is mostly true for PPDR use cases as well as for other nomadic deployments such as construction sites or logistics networks during public events.
## 3 Method for Beam Steering
### Measured Result Analysis of the Fabricated Coaxial Cable  
The 4 \(\times\) 4 array antenna designed for the experiment features uniform element spacing on both the X and Y axes, enabling the utilization of the same cables for azimuth and elevation beam steering tests. The cables were manufactured at lengths of 1021 mm, 1042 mm, and 1063 mm, relative to a reference length of 1000 mm. The manufactured coaxial cable is depicted in Figure 11. To validate the accuracy of the manufactured cables, each phase was computed using a Vector Network Analyzer (VNA). The calculated phases are presented in Figure 12. Notably, there exists a deviation of approximately 3\({}^{\circ}\) across each batch. While this may introduce some degree of inaccuracy in beam steering outcomes, it is deemed negligible for the small-scale 4 \(\times\) 4 array employed in this paper. Consequently, the phase was adjusted accordingly using the respective cables.
## 3 Results and Discussion
### 2,3-BDO Production from Glucose Semi-Defined Media
However, when levels of carbohydrates in the fermentation medium are low, 2,3-BDO can be reversibly turned into acetoin, regenerating the NADH and then keeping a continual oxidation-reduction state. When the sugar is depleted from the medium, NADH generation is decreased or stopped, and hence, 2,3-BDO is oxidized to acetoin, leading to a replenishment of NADH for cellular function [9]. Moreover, according to [PERSON] et al. [19], when glucose has been fully consumed or its levels are low, sporulation of the strain _Bacillus_ is triggered, and cells reintroduce the products generated (i.e., 2,3-BDO), metabolizing them to generate additional energy. This behavior--the re-consumption of 2,3-BDO upon substrate depletion--is well-documented in the literature for various _Bacillus_ and related species under different fermentation conditions: for example, in the 2,3-BDO and acetoin generation from glucose fermentation broth by _Bacillus amyloliquefaciens_ 83 [20]; in the 2,3-BDO fermentation of sorghum stalk biomass using _Bacillus licheniformis_ DSM 8785 [21]; in the 2,3-BDO production from carrot discard by _Paenibacillus polymyax_ DSM 365 [3]. This behavior was not appreciated for fermentation with 120 g/L initial glucose (G120), probably due to the considerable glucose levels remaining in fermentation broth along process. To mitigate this product loss and maximize the final 2,3-BDO titer, a fed-batch fermentation strategy could be implemented. This approach would involve the controlled addition of a sugar solution to maintain a minimum substrate concentration, thereby preventing the metabolic shift towards 2,3-BDO oxidation. For instance, [PERSON] et al. [21] suggest maintaining glucose levels above 10 g/L to ensure continuous 2,3-BDO accumulation and avoid its conversion back to acetoin, which is critical for simplifying downstream processing and improving process economics.
## 3 System Design
### Establishment of Zhuang Language Speech Database  
Currently, there is no publicly available Zhuang speech database. Online resources solely consist of basic Zhuang dialogues and phonetic tutorials, which are unable to fulfill the model training requirements and are difficult to obtain. Consequently, it is necessary to establish a Zhuang speech database to train the model. The Zhuang language in different regions of Guangxi is different, but the standard is Wuming Zhuang. Since Guangxi TV's Zhuang newscast speaks the standard Zhuang language, the Zhuang language data was obtained from the newscast, the subtitles were extracted, and the audio files were exported from the corresponding video, as shown in Figure 2. The following is the process of establishing the database:  
At first, it was necessary to sub-frame the video of the Guangxi TV Zhuang news program and capture all the conversations in it as much as possible. Then, the time when the conversations appear and end was determined. In addition, the video subtitle color parameter was relatively important to help generate the correct timeline information and prevent missing text. To improve the timeline accuracy, the audio that was loaded was Fourier transformed and windowed to eliminate the disturbance. Additionally, the subtitles were synchronized with the audio after being corrected. Following that, the static conversational text was extracted from the images through image processing, whereby the algorithm utilizes grayscale eroding and dilating and contour chroma-lightness similarity methods. Finally, according to the timeline information, Pydub was used to trim the video and audio components of the Zhuang news program from Guangxi TV, resulting in the extraction of the corresponding audio. The raw text was filtered to remove inappropriate content. Symbols such as (\(<\), \(>\)), [\(<\), \(\gg\) were removed. Abbreviations such as GXUTV and  
Figure 2: Text timeline correction and audio waveform extraction.  
CCTV were capitalized. Sentences that were longer than 25 characters were deleted. All text files were encoded in Utf-8.  
Guangxi TV launched a Zhuang language news program in 2013 and has now accumulated ten years of Zhuang language videos. After thorough screening and processing, a database containing 200 h of annotated Zhuang language speech can be established.
## 8 Security and Privacy Risk Assessment  
The security and privacy risk assessment helps to identify, analyze and evaluate potential security risks. This assessment helps an organization to make decisions about which risks require controls. Based on the recommendation of ISO 62304 Clause 5.2 and 5.3, this framework conducts risk assessment at the requirements analysis and system architecture phase of the development lifecycle.  
The security and privacy risk assessment are divided into two key stages; (1) Risk analysis and (2) Risk evaluation and treatment. The risk analysis stage aims to identify the assets, threats, vulnerabilities and adverse impacts on an application. To assist with the security risk analysis, an organization may use relevant information obtained from a previously risk analysis of a similar type of product as a starting point. The degree of reusability of data from previous analyses depends on the difference between the applications from a security perspective. The risk evaluation and treatment stage will identify the acceptable risks and unacceptable risks which will require controls to mitigate.
# WiFi FTM, UWB and Cellular-Based Radio Fusion for Indoor Positioning
## 7 Conclusions  
In this work, the main objective is to present multi-technology fusion with MLE as a weighting algorithm in a real scenario. Thanks to the fusion technique, the presence of multiple technologies can be used to improve location in diverse ways: with a higher precision and with a higher availability. When the number of high-precision reference points is high, fusion provides an over-determination that allows a higher precision. In cases where the number of high-precision reference points is low (for instance, in the border of deployments, or in sparse deployments), multi-technology fusion allows using low-precision and highly available technologies, such as LTE, to complement the reference points and do trilateration to achieve a high availability on the localisation service.  
Moreover, the proposed technique does not need any additional hardware apart from the receivers for each technology that will be present in most mobile devices in the near future. Thus, fusion allows to reduce costs in positioning infrastructure deployments due to a lower density requirement of high-precision devices.  
To validate the weighting technique with MLE, tests with real deployments were completed in three different Cases. MLE is presented in this paper as a technique that reduced the impact of outliers for precise positioning. Only in ideal cases with very good condition deployments, the error slightly increases. SLSQP stands out as the best search method for MLE in positioning problems.  
Conceptualisation, C.S.A.-M., H.Q.L.-C. and E.J.K.; methodology, C.S.A.-M. and H.Q.L.-C.; software, C.S.A.-M.; validation, C.S.A.-M. and H.Q.L.-C.; formal analysis, C.S.A.-M., H.Q.L.-C. and E.J.K.; investigation, C.S.A.-M., H.Q.L.-C. and E.J.K.; writing--original draft preparation, C.S.A.-M.; writing--review and editing, C.S.A.-M., E.J.K. and R.B.; visualisation, C.S.A.-M.; supervision, R.B.; funding acquisition, R.B. All authors have read and agreed to the published version of the manuscript.  
This work has been performed in the framework of the Horizon 2020 project LOCUS (grant agreement No. 871249), receiving funds from the European Union.  
Not applicable.  
The authors declare no conflict of interest.
# V2X Wireless Technology Identification Using Time-Frequency Analysis and Random Forest Classifier
## 3 Signal Model
### Nr-V2x  
The 3 GPP Release 16 defines the first specifications for the NR-V2X sidelink. It supports the same numerology and frequency bands as the NR Uplink/Downlink, but only the CP-OFDM waveform is used. A channel bandwidth up to 100 MHz is allowed in the first Frequency Range (FR1) with a subcarrier spacing ranging from 15 kHz to 60 kHz, against 400 MHz in the second one (FR2), where the subcarrier spacing parameter takes the maximum value of 120 kHz. Four modulation schemes are available, namely QPSK, 16-QAM, 64-QAM and 256-QAM [4].  
The frame structure of 5G-NR allows flexible configurations to enable novel V2X use cases. Similar to LTE, the frame length is fixed to 10 ms and is divided into ten equally sized subframes. The subframe is further subdivided into slots, depending on the used numerology. Each slot has 14 OFDM symbols, forming a typical transmission unit [29].  
Unlike LTE, the reference signals of 5G-NR are time and frequency configurable. Indeed, the DMRS, used by the receiver to produce channel estimates for data demodulation on the physical channels, is specified with a structure that has a front-load DMRS mapped in the front part of the data channel, as well as the additional mapping of 0-3 symbols ofadditional DMRS. Each design aims to find the best tradeoff between channel estimation accuracy improvement and DMRS overhead reduction [30].  
Very low latency and minimum interference with other signals is achieved with mini slot transmission. It consists of transmitting the physical channel and its DMRS over a fraction (2, 4, or 7 symbols) of the slot.
## 4 Results
### Mineralogy  
All samples contained clay, quartz, feldspar, calcite, pyrite, and other minerals. Clay minerals were the most abundant, with a content between 40.2 wt.% and 56.1 wt.% and an average of 48.92 wt.%. The second most abundant mineral was quartz, which accounted for 17 wt.% to 35.6 wt.% of the samples (average 27.79 wt.%). The content of carbonate (calcite and dolomite) was between 3.4 and 24.3 wt.%, and the content of feldspar was between 6.5 wt.% and 15.1 wt.%. The content of pyrite was low, not more than 5.5 wt.% (Table 5).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Sample**} & \multicolumn{2}{c}{**P/P\({}_{0}\) \textless{} 0.5**} & \multicolumn{4}{c}{**P/P\({}_{0}\) \textgreater{} 0.5**} \\ \cline{2-7}  & **Fitting Equation** & **R\({}^{2}\)** & **D1** & **Fitting Equation** & **R\({}^{2}\)** & **D2** \\ \hline SK1-S-1 & y = \(-0.3826\)x + 2.6889 & 0.9983 & 2.6174 & y = \(-0.2818\)x + 2.6962 & 0.9991 & 2.7182 \\ SK1-S-2 & y = \(-0.3904\)x + 2.6925 & 0.9998 & 2.6096 & y = \(-0.3156\)x + 2.6975 & 0.9988 & 2.6844 \\ SK1-S-3 & y = \(-0.4956\)x + 2.3775 & 0.9999 & 2.5044 & y = \(-0.3411\)x + 2.4528 & 0.9961 & 2.6589 \\ SK1-S-4 & y = \(-0.4541\)x + 2.6328 & 0.9998 & 2.5459 & y = \(-0.3229\)x + 2.6938 & 0.9978 & 2.6771 \\ SK1-S-5 & y = \(-0.4915\)x + 2.3774 & 0.9998 & 2.5085 & y = \(-0.3533\)x + 2.4526 & 0.9963 & 2.647 \\ SK1-S-6 & y = \(-0.4544\)x + 2.5658 & 0.9997 & 2.5456 & y = \(-0.3038\)x + 2.6377 & 0.9969 & 2.6962 \\ SK1-S-7 & y = \(-0.436\)x + 2.6787 & 0.9999 & 2.564 & y = \(-0.3181\)x + 2.733 & 0.9972 & 2.6819 \\ SK1-S-8 & y = \(-0.4724\)x + 2.4215 & 0.9999 & 2.5276 & y = \(-0.3323\)x + 2.4910 & 0.9969 & 2.668 \\ SK1-S-9 & y = \(-0.4883\)x + 2.4726 & 0.9999 & 2.5117 & y = \(-0.3145\)x + 2.5566 & 0.9972 & 2.6855 \\ SK1-S-10 & y = \(-0.5229\)x + 2.0487 & 0.9995 & 2.4771 & y = \(-0.365\)x + 2.1397 & 0.9952 & 2.635 \\ SK1-S-11 & y = \(-0.5476\)x + 1.2553 & 0.9991 & 2.4524 & y = \(-0.4832\)x + 1.3124 & 0.9978 & 2.5168 \\ SK1-S-12 & y = \(-0.4384\)x + 2.8529 & 0.9999 & 2.5616 & y = \(-0.2779\)x + 2.9299 & 0.9971 & 2.7221 \\ SK1-S-13 & y = \(-0.4069\)x + 3.1168 & 0.9998 & 2.5931 & y = \(-0.2803\)x + 3.1884 & 0.9962 & 2.7197 \\ SK1-S-14 & y = \(-0.4129\)x + 2.6306 & 0.9997 & 2.5872 & y = \(-0.2907\)x + 2.676 & 0.9996 & 2.7093 \\ SK1-S-15 & y = \(-0.5594\)x + 1.2352 & 0.9988 & 2.4406 & y = \(-0.4964\)x + 1.2921 & 0.9953 & 2.5036 \\ SK1-S-16 & y = \(-0.5533\)x + 1.6927 & 0.9979 & 2.4467 & y = \(-0.4323\)x + 1.8071 & 0.993 & 2.5677 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Fractal dimensions calculated by the FHH model.  
Figure 6: Relationship between fractal dimensions D1 and D2 of K\({}_{2}\)n\({}^{1+2}\) shale.