esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiment and Results
### Calculation of Activity Risk  
Following the calculation method proposed in Section 3.2, the historical microblogs and circle-of-friends will be subject to further sentiment analysis. The last 180 days of historical microblogs are investigated to determine whether the key user has long-lasting negative sentiment. More specifically, the key users with long-term positive sentiment or healthy friendships are be removed to reduce the false alarm rate, because they may publish a joking or cathartic microblog and do not tend to act on the sentiment. Finally, we get the results shown in Table 5.  
The value of D is calculated using Equation (14), and a negative value indicates a threat. When the value of D approaches \(-1\), the threat is high. We hence roughly divide the section of [\(-1\), \(0\)] into three parts, \(-0.7\geq\mathrm{D}\geq-1.0\) implicates a high threat, \(-0.3\geq\mathrm{D}>-0.7\) indicates a moderate threat, and \(0\geq\mathrm{D}>-0.3\) means low threat.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Positive**} & \multicolumn{3}{c}{**Negative**} \\ \cline{2-7}  & \(\mathbf{P}_{pos}\) & \(\mathbf{R}_{pos}\) & \(\mathbf{F}_{1 pos}\) & \(\mathbf{P}_{neg}\) & \(\mathbf{R}_{neg}\) & \(\mathbf{F}_{1 neg}\) \\ \hline dic & 62.5 & 69.4 & 65.7 & 65.6 & 58.3 & 61.7 \\ sim & 62.7 & 68.7 & 65.6 & 65.4 & 59.2 & 62.2 \\ dic + sim & 68.7 & 76.6 & 72.4 & 73.5 & 65.1 & 69.1 \\ dic + sim + emo & 69.7 & 77.7 & 73.5 & 74.8 & 66.2 & 70.2 \\ SVM & 68.5 & 76.3 & 72.2 & 73 & 64.1 & 68.2 \\ CNN & 67.9 & 75.4 & 71.8 & 71.5 & 63.2 & 67.6 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparisons of different methods’ outcomes.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{1}{c}{**Microblogs**} & **Violence** & **Threat Level** \\ \hline \(\mathbb{Z\times W}\) & \(\mathbb{Z\times W}\) & \(\mathbb{Z\times W}\) & \(\mathbb{Z\times W}\) & \(\mathbb{Z\times W}\) \\ \hline born to Air China’s flights, what attitude, always makes my husband & \(-1.0\) & High \\ miss the opportunity) & & & \\ \hline \(\mathbb{Z\times W}\) & \(\mathbb{Z\times W}\) & \(\mathbb{Z\times W}\) & \(\mathbb{Z\times W}\) \\ \hline smoke on the plane! & & & \\ \hline \(\mathbb{Z\times W}\) & \(\mathbb{Z\times W}\) & \(\mathbb{Z\times W}\) & \(\mathbb{Z\times W}\) \\ \hline (Buying a ticket, the airline service staff is in a bad attitude, I really want & \(-0.12\) & Low \\ to push to the airport to discuss! & \\ \hline \hline \end{tabular}
\end{table}
Table 5: Calculated threat risk.
# Preparation Techniques of TiO\({}_{2}\) Nanofluids and Challenges: A Review
## 2 Preparation Methods
### Combination Usage
To avoid cluster formation, the mixing process was carried out with the help of an ultrasonicator for 1 h in order to get better dispersion of nanoparticles in nanofluid. [PERSON] et al. [86] assessed the viscosity variation against temperature and nanoparticle volume concentration of the TiO\({}_{2}\) nanofluid formulated by a two-step method using the combination of both magnetic stirring and ultrasonication, respectively. The prepared nanofluid was stable for several months, without exhibiting any indication of particle precipitation and agglomeration. [PERSON] and [PERSON] [87] studied the effect of nanoparticle size and concentration on pressure drop and Nusselt number of the flowing nanofluid inside the counter flow heat exchanger and found an increase in Nusselt number and a power drop by increasing the Reynolds number. They also found that nanoparticles of diameter 20 nm performed best and achieved greater heat transfer rate. An ultrasonic vibrator (up to 3 h) and cetyl trimethyl ammonium bromide (0.01 %vol.) as a surfactant were used to avoid cluster formation and ensure better stability. Figure 15 shows the TEM image of TiO\({}_{2}\) nanofluids with dispersion of nanoparticles of different sizes. [PERSON] et al. [88] compared the convection heat transfer coefficients of the TiO\({}_{2}\) nanofluids prepared by using distilled water and a combination of distilled water and ethylene glycol as the base fluid and found that the value of the convection heat transfer coefficient was more when they used the combination of water and ethylene glycol. The TiO\({}_{2}\) nanoparticles and base fluid were transferred into the ultrasonic vibrator and sonicated for 4 h, a solution of NaOH was also added to the suspension to adjust its pH value to avoid agglomeration due to cluster formation. In order to get better dispersion of nanoparticles with less agglomeration, upgraded thermophysical properties, and improved stability, most researchers prefer the combination of magnetic stirring and ultrasonication to break down the clusters and pH adjustment to acquire stable nanofluids.
###### Abstract  
Functional near-infrared spectroscopy (fNIRS) is an emerging technique in studying cerebral hemodynamics; however, consensus on the analysis methods and the clinical applications has yet to be established. In this study, we demonstrate the results of a pilot fNIRS study of cerebral hemodynamic response (HR) evoked by pneumotactile and sensorimotor stimuli on the dominant hand. Our goal is to find the optimal stimulus parameters to maximally evoke HR in the primary somatosensory and motor cortices. We use a pulsatile pneumatic array of 14 tactile cells that were attached to the glabrous surface of the dominant hand, with a patterned stimulus that resembles saltation at three distinct traverse velocities [10, 25, and 45 cm/s]. NIRS optodes (16 sources; 20 detectors) are bilaterally and symmetrically placed over the pre-and post-central gyri (M1 and S1). Our objective is to identify the extent to which cerebral HR can encode the velocity of the somatosensory and/or motor stimuli. We use common spatial pattern for feature extraction and regularized-discriminant analysis for classifying the fNIRS time series into velocity classes. The classification results demonstrate discriminatory features of the fNIRS signal from each distinct stimulus velocity. The results are inconclusive regarding the velocity which evokes the highest intensity of hemodynamic response.  
Keywords:fNIRS; somatosensory; motor; sensorimotor; pneumatic tactile stimulation; hemodynamic response; stroke rehabilitation; neuroprotection; neurorehabilitation; regularized discriminant analysis; common spatial pattern +
Footnote †: journal: Applied Sciences
## 2 Localization Methods
### Fingerprint-Based Localization  
Fingerprint-based localization is a method that collects position-dependent information as fingerprints and statistically estimates the position by pattern matching [5]. As shown in Figure 2, fingerprint-based localization is largely divided into a learning phase and an estimation phase. In the learning phase, a location fingerprint database is constructed from the propagation characteristics of a radio wave source whose location and parameters are known in advance, which is observed by a sensor while moving. In the estimation phase, radio waves from an unknown target are observed and their positions are estimated by pattern matching with the positional fingerprint database constructed in the learning phase. This method is expected to further improve the accuracy of localization due to recent advances in statistics such as machine learning [12; 13].  
When actually constructing the system, it is impossible to obtain fingerprints for all observation points. Therefore, propagation characteristics are modeled, and continuous fingerprints are interpolated by regressively obtaining model parameters from propagation characteristics obtained at discrete observation points [14].  
In this study, RSSI is used as the fingerprint because it is easy to implement in hardware and does not require time synchronization. When the position coordinate of the \(k\)-th emitter is \(\mathbf{u}_{k}\) and the RSSI observed at the \(n\)-th sensor is \(P_{n}(\mathbf{u}_{k})\) [dB], the fingerprint vector \(\mathbf{F}_{k}^{\text{DB}}\) is expressed as follows where \(N\) denotes the total number of deployed sensors.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Abbreviation** & **Meanings** \\ \hline Tx & Transmitter \\ \hline Rx & Receiver \\ \hline CDF & Cumulative Density Function \\ \hline NNSS & Nearest Neighbour in Signal Space \\ \hline KNN & \(k\)-Nearest Neighbour \\ \hline ML & Maximum Likelihood \\ \hline \hline \end{tabular}
\end{table}
Table 1: _Cont._\[\mathbf{F}_{k}^{\text{DB}}=[P_{1}(\mathbf{u}_{k}),\dots,P_{N}(\mathbf{u}_{k})]. \tag{1}\]  
Next, let \(P_{n}^{\text{target}}\) [dB] be the RSSI observed by the \(n\)-th sensor from the target. The estimated position of the target \(\hat{\mathbf{u}}\) is obtained by pattern matching using the maximum likelihood estimation method with the following formula [11]:  
\[\hat{\mathbf{u}}=\arg\max_{\mathbf{u}_{k}}\sum_{n=1}^{N}\log\Bigl{(}p\Bigl{(}P _{n}^{\text{target}}\mid\mathbf{u}_{k}\Bigr{)}\Bigr{)}. \tag{2}\]  
Here, the likelihood function \(P_{n}^{\text{target}}\) is assumed to experience shadowing and its probability distribution function \(p\bigl{(}P_{n}^{\text{target}}|\mathbf{u}_{k}\bigr{)}\) follows normal distribution as follows [11].  
\[\hat{\mathbf{u}}=\arg\max_{\mathbf{u}_{k}}\sum_{n=1}^{N}\log\mathcal{N}\Bigl{(} P_{n}^{\text{target}};\mu_{n,k},\sigma_{n,k}\Bigr{)}, \tag{3}\]  
where the distribution's average and standard deviation, i.e., \(\mu_{n,k},\sigma_{n,k}\), are assumed to be obtained when calculating the ensemble average of the received signal. As described above, the target location can be estimated statistically.
# BA-YOLO for Object Detection in Satellite Remote Sensing Images
## 2 Related Work
### Object Detection
In recent years, anchor-free techniques have adopted a different approach, no longer relying on anchors, but instead, identifying key points to accomplish object detection. This method aims to reduce computational burden and simplify model design. Some notable anchor-free object detection algorithms include Cornernet [27], centerNet [28], and FSAF [29]. YOLOv8 is the latest single-stage anchor-free model in the YOLO series. YOLOv8 achieves a score of 53.9 on the COCO dataset for mAP\({}_{50-95}\), surpassing YOLOv5 with a score of 50.7. The outstanding performance of YOLOv8 in natural dataset object detection tasks proves that it serves as a foundational framework for object detection in remote sensing images.
# Enhanced QoS Routing Protocol for an Unmanned Ground Vehicle, Based on the ACO Approach
## 2 Method
### The Suggested Scheme  
In this study, each firefighting UGV is supplied with a digital map, GPS, and navigation model that gives information to the vehicle, such as the network throughput, speed, direction vector, locations, fire sensor intersections, and road segment length. Furthermore, the firefighting UGVs would use location services to establish their destinations' geographic locations, and various communication pairs would have the same QoS qualification. Moreover, at each junction, a static node (SN) is deployed as a fire sensor, to aid the in packet forwarding and routing information storage. The VANET with the ACO seeks to discover the optimum route connection, the throughput, and latency, instead of fulfilling the time limitation in the urban settings. The \(D\)\((I,E)\) graph represents the street map with a fire sensor connecting any two nearby junctions. We can clearly describe the best tracking route (y) as a row of intersections (I, I2,..., In1, In), which are connected by a set of fire sensors (c1, c2,..., cm), where \(n=n1\). It is worth noting that \"I1\" is the first junction linked to the firefighting UGV F, and \"In\" is the last intersection linked to the destination. In urban situations, the VANET supported with the ACO, aims to achieve the optimal route, the QoS, the throughput, and the latency delay limit. The fire sensor connects any two adjacent intersections given in the \(D\)\((I,E)\) urban street map. We can clearly describe the best backbone route \(y\) as a series of junctions I1, I2,..., In, which are connected by a set of fire sensors e1, e2,..., em, where \(m=n-1\). It is worth noting that \"I1\" is the first junction linked to the source firefighting UGV and \"In\" represents the last intersection linked to the firefighting UGV destination. \"I1\" has been assigned to the source terminal (ST) and \"In\" to the destination terminal (IM) (DT). Based on the foregoing, the VANET routing problem may be stated as an optimization obstacle using the ACO, with the parameters given in Equations (1)-(6), as shown in Figure 2[26].  
\[Max\;F(y)=\varphi_{1}\cdot PC(y)+\varphi_{2}\cdot PDR(y)+\varphi_{3}\cdot\frac{ D_{th}-D(y)}{D_{th}}\cdot\frac{1}{(1+D_{v}(y))} \tag{1}\]  
\[PC(y)=\prod_{i=1}^{n}PC(c_{i}) \tag{2}\]  
\[PDR(y)=\prod_{i=1}^{n}PDR(c_{i}) \tag{3}\]  
\[D(y)=\sum_{i=1}^{n}D(c_{i}) \tag{4}\]  
Figure 2: Neural network procedure for the VANET protocol boosted with the ACO algorithm.  
\[D(y)=\sum_{i=1}^{n}\frac{D_{v}(c_{i})}{n} \tag{5}\] \[D(y)\ \leq\ D_{th} \tag{6}\]  
where:  
\(F(y)\): Global QoS (from ST to DT).  
\(Pc(y),PC(ci)\): Connection probability.  
\(PDR(y),\ PDR(ci)\): Packet delivery ratio.  
\(D(y),D(ci)\): Delay.  
\(Dv(y),\ Dv(ci)\): Latency variance.  
Weight values are \(\varphi 1\), \(2\), and \(3\), therefore \(\varphi 1+\varphi 2+\varphi 3=1\).
## 4 Discussion
From Table 11, the data that are traced to the third setting of factor A (TMP) appear to deviate from normality (IBM SPSS v.29) only in the case of the COD rejection rate--a result in agreement with both statistical procedures performed (Kolmogorov-Smirnov test and Shapiro-Wilk test at \(\alpha=0.05\)). For the factor B (CFV) settings, there are occurrences when normality may be debated, mostly at the first level, while there is a split decision on the second level for both the average permeate flux and the cumulative flux decline datasets (Table 12). The normality screening results are differentiated among the settings of factor C (temperature). Both statistical tests reject the normality assumption, at the second and third level for the COD rejection rate response data and at the first level for the cumulative flux decline response data (Table 13). Finally, there are more mixed data propensities in connection to factor D (MWCO) (Table 14). Both tests agree on the digressing normality trends in the COD rejection rate response data related to the third setting of factor D, and, respectively, to the second level for the cumulative flux decline response data. The average permeate flux response data receive a split decision that it is identified to the first level. The visualization of this exhibited anisotropy, within each factor setting, is greatly facilitated by depicting factorial effects in level-partitioned violin plots. Screening for the average permeate flux response accentuates this wider diversification of the behavior of \begin{table}
\begin{tabular}{l l l l l l l l l} \hline \hline  & \multicolumn{1}{c}{**A**} & \multicolumn{3}{c}{**Kolmogorov–Smirnov Test \({}^{\mathbf{a}}\)**} & \multicolumn{3}{c}{**Shapiro–Wilk Test**} \\  & & \multicolumn{1}{c}{**Statistic**} & \multicolumn{1}{c}{**df**} & \multicolumn{1}{c}{**Sig.**} & \multicolumn{1}{c}{**Statistic**} & \multicolumn{1}{c}{**df**} & \multicolumn{1}{c}{**Sig.**} \\ \hline \multirow{4}{*}{J\({}_{\text{P}}\)} & \(-\mathbf{1}\) & 0.148 & 9 & 0.200 \({}^{\star}\) & 0.932 & 9 & 0.498 \\  & \(\mathbf{0}\) & 0.205 & 9 & 0.200 \({}^{\star}\) & 0.915 & 9 & 0.350 \\  & \(\mathbf{1}\) & 0.224 & 9 & 0.200 \({}^{\star}\) & 0.885 & 9 & 0.178 \\  & \(\mathbf{-1}\) & 0.154 & 9 & 0.200 \({}^{\star}\) & 0.957 & 9 & 0.769 \\ COD & \(\mathbf{0}\) & 0.270 & 9 & 0.058 & 0.837 & 9 & 0.053 \\  & \(\mathbf{1}\) & 0.302 & 9 & 0.017 & 0.803 & 9 & 0.022 \\  & \(\mathbf{-1}\) & 0.192 & 9 & 0.200 \({}^{\star}\) & 0.871 & 9 & 0.125 \\ SFD & \(\mathbf{0}\) & 0.267 & 9 & 0.063 & 0.851 & 9 & 0.077 \\  & \(\mathbf{1}\) & 0.233 & 9 & 0.174 & 0.872 & 9 & 0.130 \\ \hline \hline \end{tabular} \({}^{\star}\) This is a lower bound of the true significance. \({}^{\mathbf{a}}\) Lilliefors Significance Correction.  
\end{table} 
Table 11: Tests of normality (Kolmogorov–Smirnov test and Shapiro–Wilk test at \(\alpha=0.05\)) for data partitioned according to the three settings of factor A (TMP) for (A) average permeate flux (J\({}_{\text{P}}\)), (B) COD rejection rate (COD), and (C) cumulative flux decline (SFD) (IBM SPSS v.29).
###### Abstract  
This paper is about the geometric and radiometric consistency of diverse and overlapping datasets acquired with the Parrot Sequoia camera. The multispectral imagery datasets were acquired above agricultural fields in Northern Italy and radiometric calibration images were taken before each flight. Processing was performed with the Pix4 Dmapper suite following a single-block approach: images acquired in different flight missions were processed in as many projects, where different block orientation strategies were adopted and compared. Results were assessed in terms of geometric and radiometric consistency in the overlapping areas. The geometric consistency was evaluated in terms of point cloud distance using iterative closest point (ICP), while the radiometric consistency was analyzed by computing the differences between the reflectance maps and vegetation indices produced according to adopted processing strategies. For normalized difference vegetation index (NDVI), a comparison with Sentinel-2 was also made. This paper will present results obtained for two (out of several) overlapped blocks. The geometric consistency is good (root mean square error (RMSE) in the order of 0.1 m), except for when direct georeferencing is considered. Radiometric consistency instead presents larger problems, especially in some bands and in vegetation indices that have differences above 20%. The comparison with Sentinel-2 products shows a general overestimation of Sequoia data but with similar spatial variations (Pearson's correlation coefficient of about 0.7, \(p\)-value \(<2.2\times 10^{-16}\)).  
Keywords:geometric consistency; radiometric consistency; point clouds; ICP; reflectance maps; vegetation indices; Parrot Sequoia +
Footnote †: journal: Applied Sciences
# Survey of Dense Video Captioning: Techniques, Resources, and Future Perspectives
## 5 Evaluation Protocols and Metrics
### Benchmark Results
#### 5.4.1 MSVD Dataset: Method Performance Overview  
On the MSVD Dataset, various methods have been evaluated using the BLEU, METEOR, CIDEr, and ROUGE-L metrics. As shown in Table 3, several methods have reported results for this dataset, with specific models achieving notable performances. Here, we summarize the best-performing methods, along with their published scores.  
Encoder-decoder based approaches: Encoder-decoder architectures have shown promising results on the MSVD dataset. Among the recent methods, Howtocaption [76] stands out with the highest BLEU (70.4), METEOR (46.4), CIDEr (154.2), and ROUGE-L (83.2) scores. This method leverages a combination of advanced encoding and decoding techniques to generate high-quality captions. Other notable methods include KG-VCN [77] and ATMNet [78], which also report competitive scores across all metrics. Earlier works, such as VNS-GRU [79] and RMN [80], also employed encoder-decoder frameworks but achieved lower scores compared to more recent methods. This highlights the progress made in improving video description techniques over the years.  
Transformer-based approaches: Transformer-based methods have gained popularity in recent years due to their ability to capture long-range dependencies in sequences. CEMSA [33] is a notable transformer-based approach that reports competitive scores on the MSVD dataset, with BLEU (60.9), METEOR (40.5), CIDEr (117.9), and ROUGE-L (77.9). Other transformer-based methods, such as COST [41] and TextKG [32], also show promising results. However, some transformer-based methods, like SnapCap [42] and UAT-FEGs [35], report lower scores compared to encoder-decoder based approaches. This suggests thatwhile transformers are powerful architectures, their effectiveness in video description tasks may depend on the specific implementation and design choices.  
Attention-based approaches: Attention-based methods have been widely used in video description to focus on relevant parts of the video when generating captions. However, the performance of attention-based methods on the MSVD dataset varies widely. SemSynAN [103] reports relatively high scores, with BLEU (64.4), METEOR (41.9), CIDEr (111.5), and ROUGE-L (79.5). This method leverages semantic and syntactic information to generate captions. On the other hand, some attention-based methods, such as ADL [47] and T-DL [104], report lower scores. This may be due to differences in the attention mechanisms used or the overall design of the methods.
## 2 Materials and Methods  
### Description of Experimental Sites  
The Insiza and Gwanda districts form part of the Mzingwane River catchment, which flows into the Limpopo River basin (Figure 1). The Insiza district is in the natural farming region (NR) 4, which is characterized by semi-arid climatic conditions with total annual rainfall ranging between 450 and 650 mm [33]. The Gwanda district lies in NR 5, which receives an annual rainfall of less than 450 mm. Growing season rainfall is unimodal, beginning in November and ending around March/April. Minimum and maximum temperatures average between 6 and 23 \({}^{\circ}\)C and 26 and 35 \({}^{\circ}\)C, respectively. The dominant soil types in both districts are coarse-grained sands to loamy sands, and clay loans to clay with minor occurrences of vertisols [34]. The soil types are classified as Eutric/Dystic Regosols and Chronic Luvisols (FAO/United Nations Educational, Scientific and Cultural Organization (UNESCO) classification), and as Ustalfic Haplargid and Lithic/Ustic Torriorthent (Soil Taxonomy system) [35]. The landform is almost flat to undulating pediplain with some local hills and rock outcrops.  
Soil fertility varied between experimental sites depending on soil type and fertility amendments used by the different farmers (Table 1). Ten farms were selected for the research, and these farms were representative of the farming systems used in the two study districts. Smallholder farmers in the two districts use mineral fertilizer and livestock manure to improve soil fertility. The research fields were located on land with slopes of less than 2% at all farms [36; 37].  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Experimental** & \multirow{2}{*}{**pH (Water)**} & **O.C.** & **Total N** & **Total P** & **Bulk Density** \\
**Site** & & **(mg kg\({}^{-1}\))** & **(mg kg\({}^{-1}\))** & **(mg kg\({}^{-1}\))** & **(g cm\({}^{-3}\))** \\ \hline Mguni & 6.0 & 5.6 & 0.5 & 0.20 & 1.50 \\ Moyo & 5.9 & 5.8 & 0.6 & 0.06 & 1.49 \\ Mpofu & 5.2 & 4.1 & 0.3 & 0.07 & 1.46 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Initial soil chemical and physical properties (0–0.6 m) at smallholder farms used in Insiza and Gwanda districts of Zimbabwe. (Lsd = least significant difference; CV = coefficient of variation).  
Figure 1: Agro-ecological regions of Zimbabwe and location of experimental sites in Insiza and Gwanda districts of Matabeleland South province. Adapted from [12].