esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experimental Results
### Results and Discussion
#### 5.3.3 Evaluation of the CAT Approach for a Cluster-Based Predictor  
In this experiment, we fixed the CAT threshold and evaluated the performance of our proposed cluster-based CAT predictor compared with the global-based CAT predictor and the case without CAT. Figures 11-13 illustrate this comparison for the LSTM, transformer, and combined structure of both LSTM and transformer models. For the LSTM model, the MAAPE is 3.65, 3.93, and 3.21 for the cases without CAT, global-based CAT, and cluster-based CAT, respectively. For the transformer model, the MAAPE is 5.19, 5.44, and 5 for the cases without CAT, global-based CAT, and cluster-based CAT, respectively. For the combined structure of LSTM and a transformer, the MAAPE is 2.22, 3.17, and 2.96 for the cases without CAT, global-based CAT, and cluster-based CAT, respectively.  
We can observe that the cluster-based CAT approach reduces the prediction errors to the level of the baseline no-CAT approach in some models and slightly outperforms it in most cases. When comparing the cluster-based CAT to the global-based CAT approach, the cluster-based method consistently exhibits superior performance across all machine learning models, including the LSTM, transformer, and combined structure of both LSTM and a transformer. This notable improvement in predictive accuracy can be attributed to the efficacy of the clustering methodology. By grouping consumers of similar consumption patterns together, the clustering approach facilitates a more manageable and precise prediction process. The homogeneity within each cluster enhances the model's ability to capture and predict underlying patterns in the data, leading to reduced prediction errors. In contrast, the global-based approach deals with more diverse patterns, making the prediction task more challenging and less accurate.
###### Abstract  
Nowadays, both BDS-3 and Galileo can provide global positioning and navigation services. This contribution carried out a comprehensive analysis and validation of positioning performance in terms of positioning accuracy (RMS) and convergence time, which are derived from BDS-3 and Galileo precise point positioning (PPP) solutions at a global scale. Meanwhile, the comparison with GPS was demonstrated. The performance and geographical distribution of RMS and convergence time for each satellite system were analyzed. GPS outperforms the other two systems on a global scale. Galileo and BDS-3, on the other hand, only perform moderately well in certain latitude zones. The combination of dual systems related to each single system is analyzed. For the dual-system combinations, the combination of systems presents a definite advantage over Galileo and BDS-3, and this advantage is more pronounced for the kinematic PPP. For GPS, the combination with Galileo and BDS-3 has little improvement in positioning performance. For the dual-system combination based on Galileo and BDS-3, the RMS and convergence time can be improved by 50% compared with the single system. The influence of single-system kinematic PPP selection for precise products from different MGEX analysis centers on positioning performance was studied. Among the five precise products, grg products have the best positioning performance for GPS, while cod products have the best positioning performance for Galileo and BDS-3. The difference in RMS and convergence time between 2 cm and 15 min can be caused by different precise product selections.  
GNSS; PPP; constellation combinations; positioning accuracy; convergence time +
Footnote †: journal: _remote sensing_
## 2 Materials and Methods
### Giemsa  
Since Live/Dead staining could only visualize the cells on the surface of the scaffolds, Giemsa staining was applied to analyze the cell growth behavior inside the scaffolds. For this purpose, the cells were first fixed on the scaffolds after each live/dead experiment. For this purpose, the scaffolds were washed once with PBS in the first step and then incubated in a buffered 4% formaldehyde solution for ten minutes at room temperature. After ten minutes, the scaffolds were washed three times with PBS. Following this, Giemsa staining was performed. For the staining, the scaffolds were placed in a 1:10 Giemsa solution (azure-eosin-methylene blue solution) for ten minutes. The scaffolds were then washed with distilled water until no more purple dye came out. Before the scaffolds could be evaluated under a light microscope, they were cut in half with a sharp blade and air dried. Under the light microscope (Olympus SZ61 stereomicroscope), the nuclei were stained purple due to complexation of the dye with deoxyribonucleic acid.
## 4 Data Analysis and Discussion
### Text Mining Analysis (Clustering)
#### 4.2.2 Word Frequency Distribution  
By relying on the results obtained in Section 4.2.1, we adopt a word frequency distribution and a manual clustering technique per time interval. In the manual clustering, the occurrence numbers of keywords within the clusters were defined and then grouped into dimensions. This step was followed by a summation of keywords into more general dimensions for the tree time intervals. The following trend can be defined based on this kind of analysis (i.e., text analysis).  
For the same periods (i.e., 2007-2011, 2012-2015, and 2016-2019), a manual clustering process was applied where the occurrence numbers of all popular words (shown in Table 4) within the clusters were predicted and then resumed into dimensions (major concepts). This work focuses on the first time period of the selected titles. Table 4 presents the frequency of major dimensions over three time periods: 2007-2011, 2012-2015, and 2016-2019. The dimensions include mental disease, mental curing, brain, deep learning, algorithm, data mining, stability, reasoning, transmission, and machine learning. A trend line graph is also provided for each dimension to illustrate its frequency trend across the three time periods. Some cells in the table are highlighted in yellow to indicate dimensions with a significant increase in frequency over time. Although a moderate conclusion can be drawn based on the percentages of words and total words, as presented in the table, the findings should be interpreted with caution.
## 4 SARA: Main Characteristics and Architecture Components
### The SARA Client (SC)  
It represents the combination of a user and a device. The collaboration is established among different instances of these Clients. Please note that each Client may have a different final implementation, i.e., the main components must be adapted to the target client devices.  
The entry point of the SC is the _Session Manager_ component. It offers a standard way to communicate with the _Communication Service_ in terms of network managing and events handling.  
The _AR View Manager_ receives _Session_ events and is in charge of updating the AR elements that the user sees.  
The _World Center Manager_ must be implemented in such a way that a certain point of the real world is set to be the origin of the coordinate system associated with the _Session_. It is possible to implement manual policies (e.g., to allow the user selecting a certain point) or automatic (e.g., to use marker detection).  
The _App Logic_ represents the specific logic of the application embedded in this client.  
Finally, the _Interaction Manager_ component must fit the type of device in use. For example, in smartphones or tablets it must react to screen touches and to detect if some AR component was touched. If so, it asks the _Session Manager_ to send the proper event. In other devices such as HMDs for example, those events can be generated based on hand gestures, user positions or voice commands for example.  
With the idea of increasing the capabilities of SARA Clients, they may have access to _Content Provider Services_, which offer contents that can be integrated in the _Session_ status. From there, it can be shared with other clients that may have no direct connection with the content providers. Their functionality can be offered in any form, as long as clients are able to access it. Finally, _Real Time Content Providers_ and different _Content Generators_ can be exposed to the system through the _Content Provider Services_. we can imagine a scenario in which, for example, real-time external information is integrated into the session's status as Internet of Things information or social media data elements.
## 2 Methodology
### Conceptual Design of TCED  
To simulate the IPA dehydration process by extractive distillation with PG as a solvent, a flowsheet with a preconcentration column (PC), an extractive distillation column (EDC), and a solvent recovery column (SRC) was simulated in a steady state using the PRO/II process simulator. The flowsheet of the IPA-water separation TCED process is depicted in Figure 1.  
The PC is a conventional distillation column with a total condenser to provide a saturated liquid feed to the EDC. The EDC and SRC are operated under atmospheric pressure. All the columns are operated at their bubble-point temperatures. The PC's purpose is to remove a significant quantity of water from the feed as the bottom product (B1) and the IPA-water minimum-boiling azeotrope mixture as the distillate product (D1). The azeotrope is fed in the down section of the EDC, while the PG solvent is introduced in the top section of the column. The anhydrous IPA is the EDC's distillate product (D2), while in the bottom (B2), a mixture containing solvent and water is produced. The solvent is recovered in the SRC as the bottom product stream (B3). After cooling, the solvent is recycled to the EDC. The SRC's distillate (D3) is water with a minimal IPA content. A small makeup stream of extractive agent is added to balance the tiny solvent losses in the D2 and D3 distillate streams.  
In this work, the NRTL fluid package was selected to simulate the thermodynamic IPA-water-PG system [29]. Table S1 in the Supplementary File shows the binary interaction parameters of the IPA-water mixture with PG as the solvent from PRO/II.
# A Novel GNSS Interference Detection Method Based on Smoothed Pseudo-Wigner-Hough Transform
## 3 Time-Frequency Transforms
In this case, smoothed pseudo-Wigner-Ville distribution, i.e., the smoothed version of pseudo-Wigner-Ville distribution, can be obtained as [9; 11; 15; 29]:  
\[SPEC(t,\omega)=\left|STFT(t,\omega)\right|^{2} \tag{9}\]0  
where \(h\) denotes the lag window function, and \(g\) represents the time smooth window function. For example, the smoothed pseudo-Wigner-Ville distribution of the analyzed signal is shown in Figure 4e with the corresponding contour presented in Figure 4f. It can be seen that there are only three auto-terms remaining correctly in the time-frequency plane, which represent the corresponding signal components. It can be known that the cross-term with the pseudo-Wigner-Ville distribution between the pair of auto-terms located in the same time position is removed due to the use of low-pass filtering in the two-dimensional form within the smoothed pseudo-Wigner-Ville distribution; this further confirms the benefit of the smoothed pseudo-Wigner-Ville distribution method in suppressing the cross-term over the Wigner-Ville distribution and pseudo-Wigner-Ville distribution approaches. On the other hand, it can be observed that the resulting time-frequency resolution properties of the auto-terms for the multi-component signal are degraded compared with the Wigner-Ville distribution approach. Therefore, in the smoothed pseudo-Wigner-Ville distribution, the time-window overlay operation can be adopted to effectively mitigate the cross-interfering terms among different signal components; however, this benefit is accrued at the price of a deterioration of the signal's auto-terms in the time-frequency domain.
## 4 Methodology and Experimental Results
### The Dataset  
In our effort to classify sentences as either simple or complex (also referred to as sentence-level binary readability classification), we made use of the WikiSimpleWiki dataset. The dataset is available at [29], and the process through which it was created is described in [30]. The initial set (version 1.0) contained 137 K aligned sentence pairs from the Wikipedia (the free encyclopedia) corpus [31]. We chose sentences from articles that have both a simple and an original version, which when aligned, had a similarity above 0.50 (a 50% similarity threshold). The updated version contained 167 K sentences. This was achieved by aligning articles from the official English Wikipedia corpus with those from Simple Wikipedia.  
To ensure that every paragraph of a simple article was correctly aligned with that of an original article, a TF-IDF cosine similarity measurement was used, and paragraphs with a similarity below 0.50 were considered unrelated. Finally, 75 K aligned paragraphs were retrieved from the 10 K article pairings, for a total of 137K aligned sentence pairings. So, there was a hierarchy from articles to paragraphs to sentences to words. The techniques used to bisect and divide the text are analyzed in [32]. The alignment results were also evaluated by humans to check the accuracy, and out of a sample of 100 random sentence pairs, the evaluators identified 91% of them as being correctly aligned [30].  
The datasets were split into two text files, with one containing the simple sentences and the other the original ones. In our case, we used a subset of the aforementioned version 1.0 dataset. Specifically, we selected 4 K sentences from the simple set and another 4 K sentences from the complex/original set, leading to a total of 8K sentences.
## 2 Materials and Methods
### System Model
Owing to [6], the probability density function (PDF) \(f_{h_{k}}\left(h\right)\) of the unordered channel gain can be obtained by the relationship of inverse function \(r_{k}{}^{-1}\left(h_{k}\right)\) in (5). In (5), we denote \(\left|\frac{\partial r^{-1}\left(h\right)}{\partial h}\right|\) as \(F_{1}\left(h\right)\) and denote \(f_{r_{k}}\left(r^{-1}\left(h\right)\right)\) as \(F_{2}\left(h\right)\). As in (6), \(F_{1}\left(h\right)\) can be calculated and \(F_{2}\left(h\right)=f_{r_{k}}\left(r^{-1}\left(h\right)\right)\) can be transformed into \(f_{r_{k}}\left(r^{-1}\left(h\right)\right)=\)\(f_{r_{k}}\left(r_{k}\right)\). Furthermore, \(f_{r_{k}}\left(r_{k}\right)\) follows the uniform distribution of users, i.e., \(F_{2}\left(h\right)=f_{r_{k}}\left(r_{k}\right)=\frac{2r_{k}}{r_{\max}^{2}}\). Hence, the cumulative distribution function (CDF) of the unordered variable in (7) can be derived from (5) and (6), where \(r_{\max}=L\cdot\tan\psi_{FOV}\) and (8) is the field of definitions for CDF. According to [PERSON] and [PERSON] et al. [6; 24], \(C_{k\to j}\left(h_{k},a_{j}\right)\) is denoted as the rate of (4) for the \(k\)-th user to detect the \(j\)-th user message (\(j\leq k\)). When the outage occurs, the \(k\)-th user is not able to obtain their own message, which yields (9) to indicate the outage event. Figure 1:3 Figure 1:4 (10) Figure 1:5 Figure 1:6  
Using order statistics [6], the outage probability, \(P_{k}^{out}\), for the \(k\)-th user can be indicated as (10), where \(\epsilon_{k}^{*}\) is a threshold in the field of definitions, as (8) shows. Finally, the achievable user data rate is presented in (11), and our resource allocation target is to maximize the sum user data rate (i.e., system throughput) and consider the desired user data rate (i.e., QoS requirements) through rational power allocation. We note that the PDF in (5) and CDF in (7) are unordered. However, the threshold, \(\epsilon_{k}^{*}\), is difficult to decide on and (10) is ordered, which may not be directly available for dynamic user priority.
## 4 Results and Discussion
### Compression Results
#### 4.3.2 Poisson's Ratio  
Poisson's ratio of the test specimens was determined by first averaging the strains across the gauge region of the specimen, then taking the ratio of average transverse strain and average longitudinal strain. In order to minimize data noise and capture the overall behavior through the linear portion of the test, this was calculated using a linear regression for \(\bar{\epsilon}_{transverse}\) vs. \(\bar{\epsilon}_{longitudinal}\). Figure 12 shows the measured Poisson's ratio data points along with the FEA predicted ranges for the gyroid's Poisson response. Since published data for the Poisson's ratio of neat-PLA ranges from 0.3 to 0.35, it is anticipated that these gyroid specimens, being printed from PLA, should have a Poisson's ratio that falls within the \(0.3<v_{m}<0.35\) window. This data, however, shows a significant amount of scatter; enough that the variation between specimens is larger in some cases than the variation that was predicted for \(0<v_{m}<0.45\). In addition, it is clear that the double-bead thickness specimens match the FEA predictions much better than do the single-bead specimens. This, in addition to the issue with matching desired relative density, indicates that it is best to use a double-bead infill for structural predictability.  
From this data, we can see that the modulus predictions match reality quite well, and the Poisson's ratio predictions match for specimens with 2-bead wall thicknesses.
## 2 Background
### Prior Work  
The problem of maintaining reliable communication to mobile nodes through a WSN has been studied both generally and for specific mission scenarios. In such studies, especially those dealing with MAVs, communication was often assumed to be single-hop line-of-sight from the mobile node to the base station [5, 6]. As previously described, most missions which have data generated in RF-challenged environments are simply infeasible without multihop communication to the outside world. Furthermore, lack of frequency channel diversity can cause a selected transmission channel to perform poorly due to external interference or multipath fading [7]. Even brief drops in connectivity due to such interference can have severe adverse real-time performance effects during a MAV mission.  
Alternately, if relaying MAV data over a stationary multihop mesh network, nodes in the network are required to be always on - when not transmitting, each node must keep its radio in receive mode to ensure that data from the mobile node will be received by a node in the mesh [8, 9]. The need to keep nodes listening for incoming packets places a severe burden on the stationary mesh's energy budget; such persistent power consumption requires bigger, heavier batteries on each node. In a scenario where MAVs must deploy the multihop WSN mesh in an unknown environment, fewer nodes can then fit in the payload, which in turn reduces the maximum communication range of the MAV.  
Power efficiency has been addressed by medium access control (MAC) layer protocols for stationary wireless networking stacks. By scheduling wireless communications, nodes can power down their radiosentirely when not in use. These time division multiple access (TDMA) schemes allow duty cycling on the order of of 0.1%-1% or less [10; 11; 12; 13], significantly lowering the average power consumption and battery requirement of each node. These protocols can also employ frequency diversity, scheduling successive communications on different frequency channels, thus mitigating effects of radio interference. While such power efficiency and reliability has been addressed by medium access control (MAC) layer protocols in stationary wireless networking stacks, these energy efficient MAC layers have until now not been extended to include mobile nodes.  
Since line-of-sight communication can not be assumed, and current low-power WSN architectures are not suitable for MAV missions, a new architecture must be proposed to ensure low power consumption and communication reliability, while permitting rapid, high throughput data bursts when required.
# Hop-by-Hop Multipath Overlay Routing for Optimizing Network Resource Allocation in WANs
## 3 System Design
### Overlay Hop-by-Hop Routing
During this process, network resources are effectively allocated to transmissions that are unlikely to cause congestion. This method determines congestion based on the load of the links and removes the next hops that lead to unavoidable bottlenecks. The load information of the tunnel is periodically uploaded to the controller, and the controller updates the next-hop selection results based on the calculated data by Algorithm 3. The excluded next-hops are marked as congested. According to the approach in Algorithm 2, congested next hops have lower priority, which leads data towards non-congested paths and helps to effectively allocate network resources. The complexity of the algorithm for each destination is \(O(m+k)\), and the complexity for all-to-all transmission is \(O(m(m+k))\), where \(m\) is the number of overlay nodes and \(k\) is the number of overlay tunnels. We place the centralized controller at the location closest on average to all nodes. This controller periodically gathers load information of the tunnel from the network. The control delay introduced by this periodic data collection is minimal. Firstly, the periodic approach allows the system to distribute control operations over time, reducing the need for constant adjustments and thereby minimizing latency. Secondly, by strategically positioning the controller near the average center of all nodes, communication latency is further reduced. Furthermore, as observed from the experiments, the impact of Algorithm 3 on the number of paths is significantly smaller than that of Algorithm 2. Therefore, we believe that implementing Algorithm 3 on a centralized controller using a periodic method is appropriate. ```
Input: The load \(l(h)\) and cost \(c(h)\) associated with each next hop \(h\) in the next-hop set \(H(x)\) for every node \(x\in X\) towards the destination, and the DAG of routing graph G towards the destination. Output: Update \(l(h),h\in H(x)\) for every node \(x\) towards to the destination. 1\(visit\leftarrow\emptyset\);
2 queue\(\leftarrow\emptyset\);
3 for\(x\in X\)do
4 if\(\forall h\in H(x),l(h)\) indicates congestionthen
5\(visited\leftarrow\) visited\(\cup\{x\}\);
6\(queue\leftarrow queue\cup\{x\}\);
7
8 whilequeue is not emptydo
9\(x\leftarrow\) queue.pop();
10\(N\leftarrow\{n\in G:n\) has \(x\) as next hop\(\}\);
11 for\(n\in N\)do
12\(l(x)\in H(n)\leftarrow\) congestion;
13 if\(\forall h\in H(n),l(h)\) indicates congestion and \(n\
otin visited\)then
14\(visited\leftarrow\)visited\(\cup\{n\}\);
15\(queue\leftarrow queue\cup\{n\}\);
```  
**Algorithm 3**Multi-hop path selection