esa-sceva/satcom-chunk-collection huggingface dataset:
## Materials and Methods
### Wavelength Selection and Importance Assessment  
The VIS-NIR spectrum encompasses a vast range of 2101 wavelengths; however, not all of them hold significance in the classification process. To enhance the quality of the data, reduce complexity, and enhance interpretability, a meticulous selection of wavelengths was conducted. In this study, a genetic algorithm was used to select wavelengths. Stability selection, random forest, and analysis of variance methods were employed to assess the importance of the selected wavelengths. These methods were implemented using Python 3.8.13 in the PyCharm software (2021.3.2 community version, JetBrains).  
GA is a type of stochastic search algorithm that is based on the principles of genetics and natural selection [22]. GA has been proven to be effective in producing high-quality solutions for the wavelength selection problem [12]. This approach was effective in optimizing large state spaces, reducing the initial 2101 variables to a more manageable number under the maximum number of variables that were deemed to be the most influential and significant variables in the experiment.  
SS is a variable selection method based on subsampling in combination with selection algorithms [23]. In this experiment, we used the randomized lasso method to subsample the training data. Then, the coefficients of these unimportant wavelengths were compressed to zero with the lasso, achieving both accurate parameter estimation and wavelength selection. We calculated the frequency of each wavelength that was considered significant in the selections. Then, the importance of each wavelength could be evaluated with its frequency in the selections.  
RF is a robust and adaptable supervised machine learning algorithm that generates and merges various decision trees to form a decision forest and can be used for classification and regression tasks [26]. Here, RF is used as a classifier and finally returns measures of wavelength importance. RF was used to rank the importance of wavelengths in this study.  
ANOVA is a statistical technique used to compare the means of two or more groups to determine if there are significant differences among them [24]. The technique calculates an F-statistic, which represents the ratio of the variance between groups to the variance within groups. If the calculated F-value is larger than the critical value, it suggests that there is a significant difference among the group means. We calculated the F-value of each wavelength within the organic and non-organic groups; then, it was used to gauge the importance of each wavelength in the experiment.
## 1 Introduction
### Power Consumption  
As above-mentioned, satellite communication complements existing LPWAN solutions. In the market, we have good chipset solutions; for example, the CMWX1 ZZABZ-078 from Murata [17]. The chipset integrates the STM32L microcontroller from STMicroelectronics [18], and the SX1276 transceiver from SEMTECH [19]. The chipset works on 868/915 MHz bands and has a maximum budget link of 168 dB [19]. The CMWX1 ZZABZ can operates LoRaWAN and SigFox modulations. If we use this chipset as a reference, we can see that, at a voltage supply of 3.3 V, it consumes, in receive mode, around 76 mW for both SigFox and LoRaWAN. In the transmit mode, depending on the transmission power level, we have two consumption levels at a power level of 14 dBm: 155 mW and 145 mW for LoRaWAN and SigFox, respectively. When the power level is set to 20 dBm (when using antennas with low gain) it consumes 422 mW for both LoRaWAN (terrestrial or satellite communication) and SigFox modulations [16]. The ARTIC module consumes 50 mW in receive mode and 62 mW in transmit mode at an output power of 0 dBm. As we need a radiated power of 20 dBm for the VLD-A4 modulation, the use of an external power amplifier is mandatory. One existing programmable gain high efficiency power amplifier solution on the market is the RFPA0133 from Qorvo [20]. The RFPA0133 has four gain configurations at 3.6 V; and with a 0 dBm power level at its input, we have as the output: 27.5, 23, 16, and 5 dBm, with a power consumption of 828, 540, 234, and 126 mW, respectively. It is clear that the great part of the power is consumed by the power amplifier. Depending on the antenna gain, we have two possible consumptions, so that the message can be detected by the satellites: 602 and 890 mW for an antenna with a gain greater than \(-3\) dB and lower than \(-3\) dB, respectively. Most miniaturized antennas have gains lower than \(-3\) dB, so if we choose the RFPA0133, the total power consumption will be 890 mW. If we take an antenna with a gain of \(-6\) dB, we can set the transmit power level of the LoRaWAN module to 20 dBm. In this case, sending a message to the terrestrial LoRaWAN consumes half of the power consumed to send it to Kineis satellites. Thus, the combination of these two technologies to obtain a hybrid connectivity [21], can also reduce the power consumption and increase the battery life time by switching the connection between terrestrial and satellites networks. This hybrid connectivity is suitable for tracking applications, for which the design was developed. For example, for a tracking application, when the vehicle is located in towns with high buildings, or inside a garage (indoor), the satellite connectivity is not the best choice. In these two scenarios, the use of terrestrial LoRaWAN is the best choice. Satellite communication is the best solution where LPWANs are not deployed.
# Large Scale Community Detection Using a Small World Model
## 3 Preliminaries
### Small World Phenomenon  
Small world phenomenon is one of the inherent principles behind the analysis of today's large-scale social network, which indicates that any two people in the network can be linked by a small number of acquaintances [10]. It may be observed that there always exists a path of short length that can be discovered using local information. A small world network is often associated with a high clustering coefficient and its characteristic path length decreases more rapidly than the clustering coefficient as the randomness increases.  
**Definition 1**.: _A graph is said to be a small world network if average path length \(L_{avg}\) is less than or equal to the path length \(L_{rand}\) and average clustering coefficient \(C_{avg}\) is strictly less than clustering coefficient \(C_{rand}\) in a random degree distribution of the graph:_  
\[L_{avg}\preceq L_{rand}\;\;\text{and}\;\;C_{avg}\prec C_{rand}. \tag{2}\]_This phenomenon is based on the six degrees of separation principle._  
Definition 2: Six degrees of separation is the principle, which indicates that every two people in the world is connected with a chain of no more than six acquaintances.  
Small world network exhibits the following important characteristics:  
* Short Average Path Length;
* High Clustering Coefficient;
* Exhaustive search using local information.
## 3 Results and Discussion
### Sensory Analysis of Green Coffee Beans
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{Quality} & \multicolumn{4}{c}{Positive Attributes} \\  & \multirow{2}{*}{Fruity} & \multirow{2}{*}{Herbal} & \multirow{2}{*}{Sweet} & \multirow{2}{*}{Nutty} & \multirow{2}{*}{Spiced} \\ \hline High & 7.8 \(\pm\) 0.5 \({}^{\text{a}}\) & 4.5 \(\pm\) 0.4 \({}^{\text{a}}\) & 3.2 \(\pm\) 0.3 \({}^{\text{a}}\) & 2.3 \(\pm\) 0.2 \({}^{\text{a}}\) & 2.2 \(\pm\) 0.2 \({}^{\text{a}}\) \\ Medium & 5.6 \(\pm\) 0.7 \({}^{\text{b}}\) & 3.4 \(\pm\) 0.2 \({}^{\text{b}}\) & 2.5 \(\pm\) 0.1 \({}^{\text{b}}\) & 1.6 \(\pm\) 0.3 \({}^{\text{b}}\) & 1.4 \(\pm\) 0.2 \({}^{\text{b}}\) \\ Low & 3.6 \(\pm\) 0.4 \({}^{\text{c}}\) & 2.6 \(\pm\) 0.3 \({}^{\text{c}}\) & 2.0 \(\pm\) 0.1 \({}^{\text{b}}\) & 1.5 \(\pm\) 0.2 \({}^{\text{b}}\) & 1.1 \(\pm\) 0.2 \({}^{\text{b}}\) \\ \hline \hline \multirow{2}{*}{Quality} & \multicolumn{4}{c}{Negative Attributes} \\  & \multirow{2}{*}{Roasted} & \multirow{2}{*}{Smoky} & \multirow{2}{*}{Fermented} & \multirow{2}{*}{Earthy} \\ \hline High & 0.6 \(\pm\) 0.2 \({}^{\text{c}}\) & 0.5 \(\pm\) 0.1 \({}^{\text{c}}\) & n.d. & n.d. \\ Medium & 2.5 \(\pm\) 0.2 \({}^{\text{b}}\) & 1.5 \(\pm\) 0.3 \({}^{\text{b}}\) & 1.0 \(\pm\) 0.1 \({}^{\text{b}}\) & 1.5 \(\pm\) 0.3 \({}^{\text{b}}\) \\ Low & 3.6 \(\pm\) 0.2 \({}^{\text{a}}\) & 2.6 \(\pm\) 0.1 \({}^{\text{a}}\) & 2.6 \(\pm\) 0.3 \({}^{\text{a}}\) & 2.3 \(\pm\) 0.2 \({}^{\text{a}}\) \\ \hline \hline \end{tabular} n.d.: not detected.  
\end{table} 
Table 2: Sensory olfactory evaluation of green coffee beans (mean \(\pm\) standard deviation). Different small letters indicate significant statistical differences according to the coffee qualities (Tukey’s Test, \(p\) \(<\) 0.05). The results show that the sensory analysis carried out was able to identify the characteristics of the samples tested. Researchers [9] have evaluated sensory attributes in green coffee from different varieties submitted to different industrial conditions. However, the results obtained in this research can be used as a development strategy for classifying this product in the industry. Therefore, these results highlight the market potential for high-quality green coffee. Although there is wide variation of the coffee quality harvested in Colombia, it is evident that the aroma characteristics of high-quality coffee are far more acceptable than the others. Other researchers [9] have indicated that stinker alteration is produced by microbial fermentation of samples during the elaboration process that produces green coffee seeds with fermented, earthy, or putrid aromas, which makes the quality look diminished. The study findings demonstrate that the sensory analysis successfully identified the specific characteristics of the tested samples. Previous research [9] also evaluated sensory attributes in green coffee from different varieties and under various industrial conditions. These results can serve as a valuable development strategy for product classification in the industry, highlighting the potential for high-quality coffee in the market.
## 2 Volunteered Geographic Information
### The OSM Project
It also needs to be noted that a variety of map render-engines influences the creation of map \"standards\", due to their specific rendering functions that only allow certain features with particular tags to be shown on the map. The collected data and created changesets can be retrieved via the OSM web-API (only for a limited extent) or as a complete database dump for the entire world. Websites, such as Geofabrik's OSM Data Extracts, also provide data downloads for a specific area of interest. It needs to be noted that these pre-processed downloads only include the latest object versions, representing the current state of the features in the map. For analytical purposes, full history dump files are available [62], which include all versions of all features and allow for more advanced methods to test for potential changes between  
Figure 1: OpenStreetMap (OSM) project digital infrastructure and its community. IRC, Internet relay chat. different versions in the dataset. Traditionally, the OSM datasets were provided in Extensible Markup Language (XML) format. To improve performance and to allow for faster processing, a binary data format, _i.e._, Protocol Buffer Binary Format (PBF), has become more common in recent years. The success of the OSM project has been increasing in recent years, and several companies, such as Apple [63], Flickr [64] and foursquare [65], switched their mapping applications entirely or partially to OSM. Others created start-up companies that are building their solutions around OSM.
## 8 System Feasibility Discussion  
### Feasibility of the Aircraft-SB System for Tropospheric Flights  
In the previous sections, the main affecting factors on an aircraft-SB communication are demonstrated with numerical results that could help judge the feasibility of 5G and beyond network operating frequencies and services provision during flight. For flights of heights falling below 10 km in the troposphere, the impact of permanent gaseous attenuation on the signal's transmission appears clearly in the mm-wave frequency bands greater 28 GHz. 5G frequencies below 10 GHz have acceptable performance even at maximum LOS handover scenario as shown previously in Table 1. However, the gaseous attenuation has severe levels on the signals of frequencies greater than 26 GHz where starting communications using these frequencies at airport is not feasible even at clear dry sky with no fog, clouds, and rains. At heights below 10 km (troposphere height), we may use only frequencies below 10 GHz, or at most 26/28 GHz, in clear sky conditions with an extra 8 dB margin in the link budget. Other higher frequencies are exposed to high atmospheric absorption with almost impossible communication at 50 and 66 GHz frequencies even at clear sky conditions or regions. On the other hand, humid regions that are continuously exposed to heavy rain and fog/cloud occurrence, suffer from extra severe attenuation during the occurrence of these weather phenomena especially at frequencies above 10 GHz. Therefore, for regions with high availability of rain, fog/cloud occurrence, the feasible 5G frequency bands for tropospheric flights fall below 10 GHz even at heavy rain conditions and at the maximum LOS handover scenario with an extra few dB in the link budget below 10 GHz. However, at 10 GHz, the fog/cloud attenuation adds extra 32 dB along with possible 26 dB of very heavy rain attenuation may totally block the communication below tropopause heights for flights. At frequencies above 10 GHz, the rain and fog/cloud losses result in unstable communication between an aircraft and SB starting at the airport or near ground heights.
## 4 Results and Discussion  
### Experimental Setup  
Hardware and SoftwareOur DL-NMT system can be deployed onto laptops, desktops, and servers with GPUs. In this work, we run the translation system on the platforms as shown in Table 2. The table also lists the frequency, the number of cores on each CPU, whether it has GPUs, the Linux kernel, and the GCC/OpenMP version.  
Dataset DetailsWe use the news dataset from the UM-Corpus, which is a large English-Chinese parallel corpus. It provides a two million English-Chinese corpus from eight text domains, covering several topics and text genres, including Education, Laws, Microblog, News, Science, Spoken, Subtitles, and Thesis [23]. We train our models with the news subset of 252K sentences consisting of 10,635K Chinese words and 5672K English words. We split the training samples into 176,943 training pairs, 25,278 validation pairs, and 50,556 test pairs.  
Note that our web interface provides users with access to build a new corpus.
## 2 Data and Theory Description
### Theory and Method of Retrieving PWV  
#### 2.2.1 PWV Derived from GNSS  
GNSS signals are refracted and bent when passing through the troposphere [33]. Given that this delay is mainly concentrated in the troposphere, this delay is called tropospheric delay and its delay in the zenith direction is called ZTD. ZTD consists of ZHD and ZWD. ZTD can be estimated by the PPP technique or relative positioning technique using non-differenced or double-differenced GNSS observations, respectively [34]. The retrieval of PWV can be divided into three main steps.  
(1) ZHD is mainly affected by the surface pressure and can be precisely calculated by the [PERSON] model [35]:  
\[\mathrm{ZHD}=\frac{0.002277\times\mathrm{P}}{1-0.00266\times\cos(2\varphi)-0.00 028\times\mathrm{H}} \tag{1}\]  
where P is the surface pressure at GNSS station (unit: hPa), \(\varphi\) is the latitude of the GNSS station (unit: rad) and H is the ellipsoid height of GNSS stations (unit: km).  
(2) After the ZHD is obtained, ZWD can be calculated by subtracting ZHD from ZTD.  
\[\mathrm{ZWD}=\mathrm{ZTD}-\mathrm{ZHD} \tag{2}\]  
(3) Thus, the ZWD can be further converted to the PWV by multiplying the conversion factor [12]:  
\[\mathrm{PWV}=\Pi\times\mathrm{ZWD}=\frac{10^{6}}{(\mathrm{K}_{2}^{\prime}+ \mathrm{K}_{3}/\mathrm{T}_{\mathrm{m}})\times\mathrm{R}_{\mathrm{v}}\times \rho}\times\mathrm{ZWD} \tag{3}\]  
where \(\rho\) is the liquid water density (1000 kg/m\({}^{3}\)); R\({}_{\mathrm{v}}\) denotes the specific gas constant of water vapor (461.51 J \(\times\) K\({}^{-1}\)\(\times\) kg\({}^{-1}\)); K\({}_{2}^{\prime}\) and K\({}_{3}\) are atmospheric refractivity constantswith values of 16.48 K/hPa and 3.776\(\times\)10\({}^{5}\) K\({}^{2}\)/hPa, respectively. The term T\({}_{\rm{m}}\) was the atmospheric-weighted mean temperature. The T\({}_{\rm{m}}\) used in this paper was calculated based on the improved T\({}_{\rm{m}}\) model (IGPT2w) and the PWV error caused by the error of T\({}_{\rm{m}}\) is approximately 0.29 mm [36; 37].  
Given the missing meteorological sensors for some GNSS stations, the meteorological data cannot be obtained at those stations. Thus, the corresponding data (P and T) provided by ERA5 were used and the bilinear interpolation method was introduced to interpolate the gridded meteorological data to the GNSS station [38]. Vertical adjustment is extremely important in establishing tropospheric delay [39]. The empirical formulas for converting P and T at different heights are as follows [40]:  
\[{\rm{P}_{h_{1}}}={\rm{P}_{h_{2}}}\cdot(1-0.0000226\cdot({h_{1}}-{h_{2}}))^{52 25} \tag{4}\]  
\[\Delta{\rm{T}}/\Delta{\rm{h}}=-0.0065^{\circ}{\rm{C}}/{\rm{m}} \tag{5}\]  
where h was the ellipsoid height of the GNSS/RS stations (unit: m); h\({}_{1}\) and h\({}_{2}\) be unified to the same elevation systems, P\({}_{h_{1}}\) and P\({}_{h_{2}}\) were the pressure values of h\({}_{1}\) and h\({}_{2}\), respectively (unit: hPa), and T is the temperature (unit: \({}^{\circ}\)C).
# Efficient and Effective Unsupervised Entity Alignment in Large Knowledge Graphs
## 7 Conclusions  
Existing supervised EA methods primarily depend on manually curated alignment seeds, which constrains their applicability in real-world scenarios. Consequently, this paper emphasizes the exploration of unsupervised EA methods. To this end, we propose _MPGT-Align_, an unsupervised EA method based on a multi-hop pruning Graph Transformer. _MPGT-Align_ exclusively models the relation structure of KGs, resulting in a straightforward input and relaxed application requirements. To facilitate unlabeled alignment learning, we leverage pre-trained language models to derive initial features for entities while simultaneously encoding both the name text and contextual semantics of a entity. Furthermore, the pruning-hop2 Token component aggregates neighborhood features from various hops into distinct representations. This is a non-parametric method that can be executed offline before model training. Additionally, the attention-based Transformer encoder enables more expressive learning of node representations derived from multi-hop neighbors. Overall, _MPGT-Align_ not only reduces model training complexity, but also enhances the extraction of multi-hop neighborhood similarities among aligned entities. Extensive experiments demonstrate that _MPGT-Align_ achieves superior and more robust performance, consistently outperforming 26 baselines.  
We hope our work will inspire further research in this domain. In future endeavors, we aim to continue exploring similarities among aligned entities within multi-hopneighborhood structures and investigate additional alignment methodologies applicable to large KGs.  
Conceptualization, W.C.; methodology, W.C.; validation, W.C., R.Z. and W.M.; formal analysis, W.C.; investigation, W.C. and W.M.; data curation, W.C.; writing--original draft preparation, W.C.; writing--review and editing, W.C., R.Z. and W.M.; supervision, R.Z. and W.M.; project administration, W.C. and R.Z. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China (Grant No. 62306079), the Education Science Planning Project of Guangdong Province (Specialized Higher Education) (Grant No. 2023 GXJK422), and the Scientific Research Innovation Team Project of Guangdong University of Education (Grant No. 2024 KYCXTD015).  
Not applicable.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
The authors declare no conflict of interest.
## 3 Methodology
### Feature Extraction and Selection
#### 3.2.5 MFCC  
The application of Mel-frequency cepstral coefficients (MFCCs) is prevalent in speech and audio processing. The auditory features are obtained from the spectral information of an audio signal and are specifically engineered to replicate the human ear's non-linear auditory perception of sounds. The process of computing MFCCs involves several steps:  
1. Fourier Transform: The audio signal is first transformed into the frequency domain using a Fourier transform: \[X(f)=\int_{-\infty}^{\infty}x(t)e^{-j2\pi ft}\,dt\] (4) where \(x(t)\) is the time-domain audio signal, and \(X(f)\) is its frequency representation.
2. Mel Filterbank Processing: The magnitude spectrum obtained from the Fourier transform is then passed through a series of overlapping filters, typically triangular and spaced uniformly on the Mel scale. The Mel scale is a perceptual scale that approximates the human ear's response to different frequencies. The output of this stage is the energy in each Mel filter: \[E_{m}=\sum_{f=1}^{F}|X(f)|^{2}H_{m}(f)\] (5) where \(H_{m}(f)\) is the \(m\)-th Mel filter, and \(F\) is the total number of frequency bins.
3. Logarithmic Compression: The energies from the Mel filterbanks are log-transformed to mimic the logarithmic perception of amplitude and loudness in human hearing: \[L_{m}=\log(E_{m})\] (6)
4. Discrete Cosine Transform (DCT): Finally, the MFCCs are obtained by taking the Discrete Cosine Transform of the log energies. This step decorrelates the filterbank coefficients and yields a compressed representation of the filterbank energies: \[MFCC_{k}=\sum_{m=1}^{M}L_{m}\cos\left(\frac{\tau k(m-0.5)}{M}\right)\] (7) for \(k=1,2,\ldots,K\), where \(M\) is the number of Mel filters, and \(K\) is the desired number of MFCCs.  
For the research in context, a filter bank with 40 Mel filters was utilized, implying that \(M=40\) in the above equations.  
MFCCs are widely preferred in various audio and speech-processing applications because of their capability to capture the phonetically significant attributes of an audio signal while also exhibiting resilience against specific signal changes.
## 7 Implementation and Evaluation
### Multimedia Workflows
#### 7.4.2 Complete Delivery  
For flawless content delivery it must be ensured that the complete delivery time does not extend the total playing time of the movie. The complete delivery time is the time from the beginning of the playback until the last chunk is eventually received by the client device. We did experiments using 500 KB, 1 MB and 2 MB chunks while varying the number of services in the workflow.  
Figure 19: Complete delivery time and variation range.  
Figure 18: Chunk sizes und processing time.  
Figure 19 shows that with 500 KB and 1 MB chunks the time increases by small margins until a workflow length of 12 services is reached, whereas the 2 MB chunks need a nearly constant delivery time for each workflow length. The reason for this seems to be a smaller network overhead because of fewer files to transmit.  
In most cases the total processing time is much smaller than the playing duration in all workflows under considerations in our experiments. Additionally, one can see the deviation of the average of 100 measures in each case. As we can see, for the 500 KB chunks and 12 services in the workflow a time bigger than 60s is reached. But further examinations of this observations show that this occurred just in 1% of our measurements, so we can consider this as an outlier. This leads to the conclusion that a flawless playback can be expected for all examined workflow lengths.