esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experiments and Results
### False Alarms and Misjudgment  
The validity of our frame has been fully confirmed by the above comparative experiments, especially for the detection of multi-scale objects in infrared images. However, it still can be seen from the table that the improvement of the algorithm for person and car detection is not stable. Additionally, there is still much room for improving bicycle detection. Observing the whole test process, we found that false alarm and misjudgment had great influence on the test results, as shown in Figures 6 and 7.  
As can be seen from Figure 5(a)-c, most of the false alarms were errors in the recognition of small objects. This is because the infrared image resolution is too low, resulting in the scarcity of small-scale details. It is difficult to improve the discernment of the frame under complex circumstances by using OHEM for error-oriented training. It can be seen from Figure 5(d) that objects of similar shape may also be misidentified. Since the reality is that the infrared image lacks object details, it is possible to try to combine visible images in future work.  
Figure 6: False alarms occur under complex circumstances. Red lines indicate errors. Yellow lines indicate correct detections.  
Figure 7: Misjudgments occur under the complex circumstance. Red lines indicate undetected objects. Yellow boxes indicate correct detections.  
It can be seen from Figure 7a,c that for small objects with lower resolution, the network may also confuse them with the background, which makes them undetected. For cases where the objects in Figure 7b,d overlap each other, the network seems to be slightly incapable. In general, the performance of our frame for multi-scale objects is still worthy of attention.
## 4 Results and Analysis
### Numerical Simulation Results and Analysis
#### 4.1.2 Synthetic Seismic Profile  
As shown in Figure 2, a five-layer velocity model is constructed for synthesizing seismic profiles. The model's velocities are 1000 m/s, 1200 m/s, 1500 m/s, 2000 m/s, and 3000 m/s. These values correspond to 150 m, 150 m, 105 m, 105 m, and 105 m formation thicknesses. With a grid sampling interval of 5 m, the model's extent measured 1005 m (lateral) by 615 m (vertical).  
As shown in Figure 5, the seismic wave propagation is simulated using the finite difference method, and the synthetic seismic profile contains a variety of wavefield components such as direct, reflected, and multiple waves. Preliminary analysis shows that the reflection events at different stratigraphic interfaces have obvious time characteristics and energy distributions, with stronger energy in shallow reflected waves and slightly lower signal-to-noise ratios in deeper reflected waves. In addition, the strong energy of the direct wave is obvious in the shallow time interval, which interferes with the resolution of the deep reflection events. Therefore, it is necessary to remove the direct wave.
## 3 Methodology
### Dataset Creation and Annotation
Figure 1: Dataset creation workflow. Table 2 displays quantitative measures pertaining to our dataset, including the overall count of articles, as well as the standard deviation and average of daily articles, which are categorized by each target. In order to enhance the research community and guarantee the reproducibility of our procedures, we publicly release our curated dataset (due to copyright restrictions, this dataset only includes URLs of headlines; the actual text of the headlines cannot be released. However, the URLs can be used by users with access to a Bloom \begin{table}
\begin{tabular}{c c c} \hline
**Target** & **Headline** & **Label** \\ \hline Amazon & Pioneer Disciplined Growth Buys More Nike Class & \(-1\) \\ Amazon & B, Cuts Amazon & \(-1\) \\ Amazon & Amazon to Invest Over \$440 m to Boost Delivery & \(1\) \\  & Drivers’ Pay & \(1\) \\ Netflix & Netflix, [PERSON] Win \$20 Million California Film & \(1\) \\  & Tax Credits & \(1\) \\  & Netflix Shares Dip Despite Apple’s Surge in & \(-1\) \\  & Streaming Subscribers & \(-1\) \\  & Netflix & Netflix CFO Speaks at Bank of America Conference & \(0\) \\  & Nvidia to Partner with India’s Tata, Reliance for AI & \(1\) \\  & Development & \(1\) \\  & Neuberger Berman Guardian Buys More Walmart, & \(-1\) \\  & Cuts Nvidia & \(-1\) \\  & Alphabet & Alphabet Hits \(52\)-Week High at \(\$139.17\) & \(1\) \\ \hline \end{tabular}
\end{table} 
Table 1: Examples of annotated headlines. \begin{table}
\begin{tabular}{c c c} \hline
**Target** & **Articles** & **Daily Articles** \\ \hline Amazon & \(573\) & \(5.07\) (\(4.41\)) \\ Netflix & \(298\) & \(3.77\) (\(5.14\)) \\ Nvidia & \(392\) & \(3.70\) (\(4.28\)) \\ Alphabet & \(213\) & \(3.04\) (\(3.95\)) \\ \hline Total & \(1476\) & \(15.58\) (\(17.78\)) \\ \hline \end{tabular}
\end{table} 
Table 2: Dataset statistics. Figure 2: A visual representation of sentiment distribution of news headlines for the targets in the dataset. the dataset. The dataset is available at: [[https://github.com/iftikharm895/Target-Level_Financial_Sentiment_Analysis--accessed](https://github.com/iftikharm895/Target-Level_Financial_Sentiment_Analysis--accessed)]([https://github.com/iftikharm895/Target-Level_Financial_Sentiment_Analysis--accessed](https://github.com/iftikharm895/Target-Level_Financial_Sentiment_Analysis--accessed)) on 23 December 2024). This dataset, consisting of news headlines related to prominent stock companies and their relevant sentiments, is a substantial resource and is anticipated to be highly beneficial for researchers and practitioners who specialize in utilizing machine learning methods for target-level financial sentiment analysis. This work is expected to foster additional study in this field, thereby enhancing our collective comprehension of the complex connections between market behavior, AI models, and financial news sentiment.
# Transfer Learning for Multi-Premise Entailment with Relationship Processing Module
## 2 Related Works
In the research content of this paper, the target task MPE with multiple premises is different from the source task SPE with a single premise in the form of data. We use the simplest Siamese framework [22] as an example. We need an encoding parameter sharing layer (such as LSTM [23] or CNN [24]) to encode the premise and hypothesis separately, and then use an inference layer ( such as a full connection and softmax function) for three classifications. In the Siamese framework, it is generally believed that the encoding layer can extract syntactic and semantic information [25] and convert them into feature vectors, while the inference layer can project feature vectors nonlinearly into distinguishable spaces. Because this space may be unique in each data set, the classification layer is often considered only suitable for a specific corpus task, which makes it difficult for the classification layer to use after transferring directly [21]. In this case, it is an effective method to use target data to fine-tune the pre-trained model. In transferring from SPE to MPE, the focus is on how to deal with multiple premises in the fine-tuning process. Given the existence of an SPE model based on the Siamese framework as Figure 3, we summarize the following transferring scenarios:  
* Data form transformation: Concatenating multiple premises together to form a single premise [1; 26] ;
* Statistics of results after matching: Each premise and hypothesis is matched separately, and statistics of results after matching are made to obtain the final result [1] ;  
Neither of these two schemes made any changes to the structure of the pre-trained model, we only need to fine-tune the pre-trained model with the target training data, but both of them have their disadvantages: First of all, the data form transformation is an attempt to convert the MPE task directly from the data level to the SPE task, which is the simplest, requiring simple preprocessing of multi-premise data. However, with the number of premises, the concatenated single premise will form a long text. In the face of the tens of thousands of premises that may exist, the encoding layer may have difficulty understanding such complex semantics. Encoders such as LSTM or Transformer [27] will consume much running time and memory when processing such a long text, which is why we do not recommend adopting large-scale pre-trained models such as Bert to do this task. The statistics of results after matching, similar to the premise-wise sum of experts (SE) model [1], take advantage of the MPE data characteristics, and treat the MPEmodel as an ensemble of the SPE model. However, due to the third characteristic of MPE data(in Section 1), we do not consider this a suitable method. Because each premise in the multi-premise only has incomplete information, each match is based on the judgment of the incomplete information, which will cause all matches to be misled, and the conclusions drawn from multiple misleading combinations will deviate from the correct answer.
# Massive MIMO Indoor Transmissions at 38 and 65 GHz Applying Novel HBF Techniques for 5G
###### Abstract  
The 5G Infrastructure Public Private Partnership (5 GPPP) has recently published a white paper about 5G service indoors, since up to now, it had mainly focused on the outdoors. In an indoor environment, the requirements are different since the propagation mechanism differs from other scenarios. Furthermore, previous works have shown that space frequency block code (SFBC) techniques applied to multiple antennas improve performance compared to single-input single-output (SISO) systems. This paper presents an experimental study in an indoor environment regarding the performance of a massive multiple-input multiple-output (mMIMO) millimeter-wave (mmWave) system based on the 5G New Radio (NR) standard in two frequency bands. In a first step, the 38 and 65 GHz bands are compared by applying a low-complexity hybrid beamforming (HBF) algorithm. In a second step, the throughput and the maximum achievable distance are studied using a new algorithm that combines the SFBC technique and HBF. Results show, at 38 GHz with HBF and aggregated bandwidths (4 \(\times\) 100 MHz), a maximum throughput of 4.30 Gbit/s up to 4.1 m. At 65 GHz, the SFBC + HBF algorithm improves the communication distance by 1.34, 1.61, or 1.75 m for bandwidths of 100, 200, or 400 MHz, respectively.  
massive MIMO; mmWave; 38 GHz; 65 GHz; indoor communication; 5G; SFBC; hybrid beamforming 222, 23716. [[https://doi.org/10.3390/s22103716](https://doi.org/10.3390/s22103716)]([https://doi.org/10.3390/s22103716](https://doi.org/10.3390/s22103716))
## 2 Materials and Methods  
Following a power analysis for sample size calculation as described below, 55 patients (34 females (61.8%), 21 males (38.2%)), 7-16 years old, seeking orthodontic treatment at the Postgraduate Orthodontic Clinic of the Department of Orthodontics of the Faculty of Dentistry, School of Health Sciences, Aristotle University of Thessaloniki, Greece were included in this cross-sectional clinical study. The inclusion criteria included (1) mixed or permanent dentition with fully erupted upper permanent central incisors, (2) good general health without nutrition issues, (3) absence of use of anti-inflammatory or antibiotic drugs during the last month prior to the examination, (4) good oral hygiene and healthy periodontium with plaque index and bleeding index < 25%, and (5) absence of any kind of previous orthodontic treatment or orthodontic tooth movement. The exclusion criteria included (1) the presence of any chronic disease, hormonal disorder, or condition that may affect growth and (2) the intake of any medication that affects bone metabolism upon entering the study or in the past. Patients' types of malocclusion or skeletal discrepancy were not taken into consideration during patient selection. The study protocol was approved by the Health Sciences Institutional Review Board (Ethical Committee of the Faculty of Dentistry, School of Health Sciences, Aristotle University of Thessaloniki, Greece) (#406/17.07.2019). In addition, written informed consent was obtained from all participants included in the study and their legal guardians.  
Patients enrolled in this study had professional supragingival scaling and polishing of their teeth 2 weeks before participating in the study. At that time, oral hygiene instructions and instructions to refrain from eating or drinking two hours prior to saliva collection were given to them.  
Lateral cephalometric radiographs were available in the context of collecting all the initial diagnostic data prior to the orthodontic treatment and were used in order to examine the skeletal age of each patient by applying the CVM method on the cervical vertebrae. The lateral cephalometric radiographs were obtained with the same Panoramic X-ray Unit (Planmeca Proline XC, Planmeca OY 00880, Helsinki, Finland) and identical parameters were used (68 kV, 5 mA).
# Composition and Basic Physical Properties of the Phobos Surface: A Comprehensive Review
## 6 Characteristics and Physical Properties of Phobos Regolith
### Physical Properties of Regolith
#### 6.2.2 Density
When using the same volume but considering the masses from the MEX 2010 and 2013 flybys [69], the volumetric density was found to be 1847 kg/m\({}^{3}\) (\(\pm\)11 kg/m\({}^{3}\)) (Table 3, Figure 20). For the GM determined within the uncertainty range, which accounts for most solutions from flybys, gravity field derivations, and \"distant\" encounters [8], or the ROB (Royal Observatory of Belgium) solution from the close MEX 2008 flyby but with a range of \(\pm\)3 \(\times\) 0.03 \(\times\) 10\({}^{-4}\) km\({}^{3}\cdot\)s\({}^{-2}\), the volumetric density was calculated to be 1855 kg/m\({}^{3}\) (\(\pm\)26 kg/m\({}^{3}\)). Comparing the volume density of Phobos to the density of other asteroids and meteorites (Figure 21), it can be observed that it is similar to the density of low-albedo carbonaceous asteroids (falling roughly in the middle of their density range, Figure 21). Available volume density data for asteroids indicate that they are generally lower than the density of their carbonaceous meteorite analogs, much like the volume density of Phobos, which is also lower than most carbonaceous material samples (Figure 20). Additionally, based on surface observations of Phobos conducted by the Arecibo radar system, [PERSON] et al. [75] estimated the volume density of the uppermost layer to be approximately 1600 \(\pm\) 300 kg/m\({}^{3}\). This density is similar to the density of Cl chondritic meteorites Cl (1.6 \(\pm\) 0.03 g/cm\({}^{3}\)) [70] and the Tagish Lake meteorite (1.64 \(\pm\) 0.02 g/cm\({}^{3}\)) [76] (Figure 21), which is most likely a fragment of a D-type asteroid. Figure 21: The volume density of Phobos (average density, Table 3) compared with low-albedo carbonaceous asteroids (C-type asteroids) [77], chondritic meteorites [76], and Martian meteorites (SNC) [73].
## 2 Background and the Related Work
### ML-Based Approach  
The use of traffic classification techniques based on ML has recently solved the drawbacks of traditional traffic classification methods. In this method, an ML classifier is trained as input, and then unknown classes are identified using the trained sample prediction. The ML-based technique is further classified into three categories (supervised, unsupervised, and semi-supervised) based on the mechanism followed by the model and the requirement of training data [25].
## 3 Materials and Methods
### Data Analysis  
All test outcomes were tabulated and statistically analyzed using Python v3.13 (NumPy, SciPy) and MATLAB vR2024b. Averages, standard deviations, and 95% confidence intervals were calculated for each metric. Intergroup comparisons were performed using a two-sample t-test with a significance level of \(\alpha=0.05\). Correlations between parameters (e.g., layer height vs. \(R_{a}\)) were also explored to identify potential relationships within each technology.
## 3 Methods
\(T\) is the expected output value according to the input data [36] and \(j\) is the number assigned to the node in each layer. \(n\) is the number of output layers. \(E_{total}\) is minimized through the updated weight from Equation (16). To detect the minimum point, differentiation of \(E_{total}\) is necessary. Thus, an activation function is needed for conversion from the result of real number \(x\) to a range of values between 0 and 1 when the value is passed through the next layer or when the final result is expected. The unipolar sigmoid function was used for the activation function in this study. The equation of the unipolar sigmoid function is as follows. \[f(x)=\frac{1}{1+e^{-x}} \tag{3}\]  
In this study, the ANN was supported by MATLAB software. Using the training data of randomly extracted habitat sample data, the slope data of the flat area were used as non-habitat area for training, and the input data of the study area were rearranged between 0.1 and 0.9. The weights of the input layers were calculated using the back-propagation algorithm of the ANN with a \(9\times 18\times 1\) structure of the networks. The initial weight was set randomly as described previously, and the learning rate and number of epochs were set to 0.01 and 1000, respectively. The root-mean-square error, the criterion of the stopping point of decreasing error, was set to 0.01. The process of potential mapping for martenss and leopard cats can be explained in three steps, as shown in Figure 4. First, a spatial database was constructed with nine potential habitat-influencing factors, including the observation points of martenss and leopard cats. Half of the marten and leopard distribution data selected by random sampling were used as training sets. The locations of observations of martenss and leopard cats were confirmed in field surveys for the application and validation of habitat potential models described in Section 2.2 (Figure 2). The observation location data of martenss and leopard cats were set as dependent variables. Second, the ANN model was applied to a spatial database to map the habitat potentials. Nine factors that were considered to affect the marten and leopard cat habitats were set as independent variables. Elevation, slope, and aspect derived from topographic maps, timber type and timber age from forest maps, and land cover, distance from road, distance from water, and distance from forest from land cover map were included in the spatial database with half of the randomly sampled marten and leopard cat habitat data. The ANN model was applied after constructing the spatial database. To apply the model, MATLAB programming was used with the GIS data. The resulting maps of the habitat potentials were validated using a receiver operating characteristic (ROC) curve. Finally, validation of the marten and leopard cat habitat potential maps was performed using the remaining marten and leopard cat distribution data that were not included in the spatial database. Figure 4: Flow chart of the steps used in this study.
## 3 Results and Discussion
### Synthesis of Zeolites
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Component** & **Content** & **HG/T 3061-2009** & **Result** \\ \hline SiO\({}_{2}\) content & 93.43\% & \(\geq\)90\% & Qualified \\ Heating reduction & 5.33\% & 4.0–8.0\% & Qualified \\ Ignition reduction & 6.67\% & \(\sim\)7.0\% & Qualified \\ Fe content & 0.034\% & \(\leq\)0.05\% & Qualified \\ Mn content & 0.0029\% & \(\leq\)0.004\% & Qualified \\ Cu content & 0.0008\% & \(\leq\)0.001\% & Qualified \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performance test of precipitated silica. as its heterogeneous crystallization center, nucleated on the surface of NaA crystal, and developed into cluster-shaped aggregates. This was due to the fact that polar solvent ions were easily adsorbed on the grain surface in the alkaline hydrothermal system [24], and the growth into aggregate could overcome the nucleation barrier. When the crystallization time was further extended to 5 h, the complete disappearance of the NaA zeolite and the whole formation ofodalite occurred, showing the spherical shape as a whole which was agreeable with the literature description [25]. Therefore, both XRD and SEM analyses evidenced that the increased crystallization time at an elevated temperature accelerated the structure changes from NaA toward sodalite. The longer the time, the more sodalite is present in the zeolite products. The crystallization time of 5 h significantly increased the zeolite yield to be as high as 410.3 g\(\cdot\)kg\({}^{-1}\). \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \multirow{2}{*}{**Crystalline**} & \multirow{2}{*}{**Crystal Face (d/nm)**} & \multicolumn{8}{c}{**Sample**} \\ \cline{3-10}  & & **0 h** & **0 h—2** & **1 h** & **1 h—2** & **3 h** & **3 h—2** & **5 h** & **5 h—2** \\ \hline \multirow{4}{*}{NaA zeolite} & 622 & 0.7411 & 0.7406 & 0.7413 & 0.7417 & 0.7422 & 0.7431 & - & - \\  & 642 & 0.6573 & 0.6574 & 0.6585 & 0.6576 & 0.6606 & 0.6594 & - & - \\  & 644 & 0.5964 & 0.5966 & 0.5970 & 0.5968 & 0.5986 & 0.5979 & - & - \\  & 664 & 0.5242 & 0.5242 & 0.5248 & 0.5245 & 0.5255 & 0.5252 & - & - \\ \hline \multirow{4}{*}{Sodalite zeolite} & 110 & - & - & - & - & 1.2657 & 1.2655 & 1.2659 & 1.2660 \\  & 211 & - & - & - & - & 0.7286 & 0.7284 & 0.7290 & 0.7294 \\ \cline{1-1}  & 222 & - & - & - & - & 0.5152 & 0.5146 & 0.5153 & 0.5158 \\ \hline \end{tabular}
\end{table} 
Table 5: D-spacing of zeolites synthesized at different crystallization times (based on XRD results). Figure 4: XRD images of zeolites synthesized at different crystallization times. (**a**,**b**) The sample XRD results of two parallel experiments, respectively (**S—**sodalite zeolite; A—**NaA zeolite).