esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Model Validation  
It is quite challenging to verify the model of the cylindrical spliced trough system because there are currently very few articles discussing and researching this type of trough system, and no similar systems are in operation. In this paper, we use the ray-tracing method for calculations, which differs significantly from the cone optics method we used previously. Since the intercept factor is a key parameter determining the efficiency of the parabolic trough system, to verify the accuracy of this method, we used the ray-tracing method mentioned in this paper, the cone optics method [21] and SolTrace (3.1.0) software [35] to calculate the intercept factor of the parabolic trough system at different receiver half-widths, as shown in Figure 2. We used the same trough system data for all methods, with the sun's half-angle set at 4.65 mrad and modeled as a Gaussian distribution. The optical errors in the x and y directions were 6 mrad, and the trough half-width was set to 4 m. To simplify the model, we assumed that light rays were incident perpendicularly and that all rays incident on the mirror surface were fully reflected.  
The calculation results showed that the average intercept factors obtained from the ray-tracing method, cone method and SolTrace simulation were 61.86%, 61.86% and 61.68%, respectively, the proposed ray-tracing method took 0.1% compared to the conventional ray-tracing method, the cone method took 3.7 s, and the Soltrace took 0.165 s. The differences  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Data** \\ \hline Focus length \(f_{0}\) & 2 m (rim angle = 90\({}^{\circ}\)), 4.8 m (rim angle = 45\({}^{\circ}\)) \\ Half trough width \(w\) & 4 m \\ Length of trough \(L\) & 100 m \\ The half the width of the receiver \(r_{0}\) & 0.04 m \\ Radius of envelope of receiver \(R\) & 0.055 m \\ Operation temperature of receiver \(T\) & 400 \({}^{\circ}\)C \\ Cylinder slope error \(\theta_{slope}\) & 1 mrad \\ Parabolic slope error \(\theta_{slope}\) & 3 mrad \\ The other error \(\delta_{other}\) & 1 mrad \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameter of the typical parabolic trough solar collector.  
between the two calculation methods were minimal, with the maximum intercept factor error in the calculations being less than 0.026%. Additionally, the simulation results closely matched the SolTrace test results, with the maximum absolute deviation between the two methods being less than 0.31%. Therefore, the optical model used in this paper can effectively simulate the performance of a parabolic trough solar system.
# Sentiment Analysis of Twitter Data
## 2 Twitter  
Various microblogging platforms like Twitter, Facebook, and Instagram were born out of the emergence of SNS [14]. Twitter is a widely used SNS that allows users to exchange 140-character messages (referred to as \"tweet\") [16]. More than 300 million people have signed up to use Twitter, which generates over 500 million updates each day [6; 17]. Because of the ease with which it can be shared, Twitter has grown to be one of the most important sources of user-generated data. The following is a list of the most important features of Twitter.  
**Tweet**: A tweet is a 140-character maximum data unit that can be transmitted using Twitter. Its content ranges from how people feel or what they think about certain events, to photos, videos, and links, etc., all of which can be easily shared with the users' contacts.  
**Handle**: This refers to the behavior of tweet updating or public messaging to other users. It is written as \"@username,\" and the @ symbol is used to refer to the person or organization with whom the tweets are connected [14].  
**Hashtag**: Hashtag is a kind of metadata tag used in various SNS that allows users to adopt dynamic, user-generated tags to make it easier for others to find the tweets related to a specific topic [18].  
**Follow**: This is an activity of registered users to pursue people, companies, or any organization that they are interested in and to receive updated tweets in real time. Twitter is more than just a tool for staying in touch with friends and sharing one's own daily activities, its true strength lies in the dissemination of information and the following of others.  
**Retweet**: It is one of the most useful tools for disseminating information on Twitter, in which users are allowed to re-post the tweets they are interested in. Here, the original tweets generally remain unchanged, followed by the abbreviation of the original username of the authors [14].  
**Search**: This powerful feature allows users to search keywords and phrases on Twitter to find updated tweets about their interests in real time [19]. People are more likely to join Twitter because of this search function, which facilitates the discovery and dissemination of relevant content.  
Table 2 shows an example of a tweet from the user, BaskFan. It is worth noting that the tweet contains some of the features above. @Strive indicates that the tweet is a reply to the user of Strive, and the user, NBA, has also been mentioned. Meanwhile, the hashtag shows that it is related to the topic of lakers.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Source** & **Username** & **Post** \\ \hline Twitter & BaskFan & @Strive: I LIKE watching basketball @NBA game especially LAKERS GAMES. \#lakers \\ \hline \hline \end{tabular}
\end{table}
Table 2: One example of a tweet including user opinions.
# Evaluating 60 GHz FWA Deployments for Urban and Rural Environments in Belgium
## 4 Network Planning and Optimisation
### Multi-Objective Methods
\(C_{1}\) defines that a CPE can be connected to only one EN. \(C_{2}\) specifies that the number of resource blocks (RB) used per EN must not exceed the maximum capacity of 10,800 RBs or its equivalent maximum bit rate. \(C_{3}\) specifies that all ENs in the networks need a possible, stable connection with at least another EN. \(C_{4}\) indicates that the PoP needs at least one connection with some ENs. \(C_{5}\) restricts the path loss of the links to be used only if they are smaller than the maximum supportable path loss for the defined minimum bit rate, which is 1 Gbps for the CPE, 2.5 Gbps for the ENs and 4 Gbps for the PoP. Lastly, \(C_{6}\) defines the normalisation of the weights in the minimisation function.
## 3 Results
### Dataset  
We select the aircraft dataset from DOTA [30] and UCAS-AOD [31] to train and test the proposed model. Rather than the horizontal box in previous datasets, each instance is annotated with an oriented bounding box, which provides more accurate location and size of the object.  
The DOTA image size, ranging from around 800 \(\times\) 800 to 4000 \(\times\) 4000 pixels, contains aircrafts that exhibit a great variety of scales, orientations, and shapes. Specifically, the DOTA dataset maintains a large number of small instances and provides large objects over 500 pixels as well. In short, DOTA provides a reliable benchmark for studying multi-scale and arbitrary-oriented object detection in aerial images. We selected 79 images containing 3509 objects from DOTA.  
In UCAS-AOD, we selected 1000 aircraft images containing 7482 objects. All objects in UCAS-AOD were labeled with both oriented bounding boxes. In our experiments, we randomly divided the training and test set by 8:2.
# In-Depth Analysis of GAF-Net: Comparative Fusion Approaches in Video-Based Person Re-Identification
## 3 Proposed Method
### Appearance Feature Extraction Module
#### 3.2.3 Pyramid in Transformer (PiT)  
The Pyramid in Transformer (PiT) model, introduced by [PERSON] et al. in [10], marks a significant advancement in video-based pedestrian retrieval. Relying on transformer models, PiT adeptly handles complex visual data hierarchies. Its multi-directional and multi-scale approach generates comprehensive representations of pedestrians, which are crucial for accurate person Re-ID under a variety of environmental conditions and for various camera perspectives.  
PiT first segments each pedestrian image into patches, which are then converted into embeddings. These embeddings are processed through multiple transformer layers, employing strategies like no division, as well as vertical, horizontal, and patch-based divisions. This enables PiT to capture different ranges of features, enabling easier recognition of individuals across different poses and environmental settings.  
For each image, PiT constructs a pyramid by combining features from various layers (or divisions, such as vertical, horizontal, etc.). The final representation is derived by averaging these pyramids across all images.  
The combination of multi-directional and multi-scale features yields the output vector (\(\phi^{\text{PIT}}(\mathbf{A}^{i})\)) such that  
\[\mathbf{z}^{i}_{\text{PIT}}=\phi^{\text{PIT}}(\mathbf{A}^{i}), \tag{6}\]  
were \(\mathbf{A}^{i}\) is the tensor that contains the appearance data. The feature vector (\(\mathbf{z}^{i}_{\text{PIT}}\)) offers a multi-faceted view of the individual's appearance, enabling more accurate and efficient person re-identification, even in complex and dynamic visual environments.
# Spatiotemporal Variation of Land Surface Temperature Retrieved from FY-3D MERSI-II Data in Pakistan
## 3 Materials and Methods
### Calculation of the LSE for FY-3D MERSI-II  
After the emissivity of bare soil \(\epsilon\mathrm{s_{i}}\) for the FY-3D MERSI-II TIR bands 24 and 25 were calculated from the bare soil emissivity of the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) TIR bands 13 \(\epsilon\mathrm{s_{13}}\) and 14 \(\epsilon\mathrm{s_{14}}\), the land surface emissivity \(\epsilon_{\mathrm{i}}\) of each FY-3D MERSI-II pixel could be further estimated with the mixed pixel emissivity model, the FY-3D MERSI-II vegetation emissivity \(\epsilon\mathrm{v_{i}}\), and the corresponding vegetation cover fraction \(\mathrm{P_{v}}\) had been calculated from the FY-3D MERSI-II NDVI data. According to [PERSON] et al. [7] the emissivity for the FY-3D MERSI-II data can be calculated as follows:  
\[\epsilon_{\mathrm{i}}=\epsilon\mathrm{v_{i}}\;\mathrm{P_{v}}+\epsilon\mathrm{ s_{i}}\;(1-\mathrm{P_{v}}) \tag{12}\]  
where \(\epsilon_{\mathrm{i}}\) is the LSE of the FY-3D MERSI-II data and \(\epsilon\mathrm{v_{i}}\) is the vegetation emissivity used to estimate the LSE for FY-3D MERSI-II TIR bands 24 and 25, which has the value as \(\epsilon\mathrm{v_{24}}=0.982\) and \(\epsilon\mathrm{v_{25}}=0.984\), respectively; \(\epsilon\mathrm{s_{i}}\) is the bare soil emissivity for LSE estimation. The FY-3D MERSI-II multi-year average bare soil emissivity image could be obtained from the ASTER (Global Emissivity Dataset) GED database's mean emissivity to replace the bare soil emissivity constant usually used in the VCM method; this can represent changes in bare soil's emissivity in space, enhancing the precision of emissivity inversion.  
In terms of the following, LSE for a pixel can be simply understood as a weighted mix of vegetation and bare soil emissivity [36], as follows:  
\[\epsilon_{\mathrm{i}}=\epsilon\mathrm{v_{i}}\;\mathrm{P_{v}}+\epsilon\mathrm{ s_{i}}\;(1-\mathrm{P_{v}}) \tag{13}\]  
where \(\epsilon_{\mathrm{i}}\) is the emissivity, \(\epsilon\mathrm{s_{i}}\) and \(\epsilon\mathrm{v_{i}}\) represent the emissivity of bare soil and vegetation in channel i, and \(\mathrm{P_{v}}\) is the proportional of vegetation, which is calculated as follows:  
\[\mathrm{P_{v}}=[(\mathrm{NDVI_{a}}-\mathrm{NDVI_{min}})/(\mathrm{NDVI_{max}}- \mathrm{NDVI_{min}})] \tag{14}\]  
\(\mathrm{NDVI_{min}}\) represents the value of bare soil and \(\mathrm{NDVI_{max}}\) represents the value of vegetation.  
The emissivity values of each surface type (i.e., bare soil and vegetated portion) were traditionally determined using spectral library data, which is accurate for the vegetated portion's high and low-contrast emissivity. However, soil emissivity in the TIR bands may vary significantly due to the various mineral components, soil moisture content, and surface roughness. Therefore, it is necessary to estimate each pixel's emissivity of the soil type component. A detailed description can be found in the works of [PERSON] et al. [37; 38], to utilize the ASTER GED Product for thermal bands.
# Hydraulic Performance of Wave-Type Flow at a Sill-Controlled Stilling Basin
## 3 Results and Discussion
### Pressure Head
The pressure head increased continually and reached the maximum closest to the upstream face of the sill. The resultant first peak value was much larger than the downstream depth. For the wave impact zone, wave-type flows induced a significant water drop downstream of the sill and resulted in a sudden pressure decrease and a secondary peak. The peak pressure value is still very high compared to downstream stable flow conditions \(y_{2}\) (e.g., \(H_{\rm p}/y_{2}\) approximates 2.0 times \(y_{2}\) when \(q_{w}=0.205\) m\({}^{2}\)/s for Case M31). After the wave impact, the pressure head gradually decreased to a constant value. The pressure profile for a classical hydraulic jump by a sill--for which its position was at sta./\(y_{2}=5.71\), 5.79, 5.87 at a unit discharge of 0.24 m\({}^{2}\)/s, 0.46 m\({}^{2}\)/s, and 0.93 m\({}^{2}\)/s, respectively--is also illustrated in Figure 8f. Apart from the apparent pressure drop and wave impact in the vicinity of the sill, the mean pressure downstream of the sill was quasi-hydrostatic [21]. Generally, the designers and contractors should reinforce the stilling basin with concrete and steel to prevent the scouring of the bedrock, particularly for the local peak pressure [22]. The relative first and second peak pressure head in the vicinity of the sill normalized by the sill height \(H_{\rm p}/\)s can be expressed in terms of dimensionless variables as  
\[\frac{H_{p}}{s}=aF_{1}^{b}L^{c}S^{d}, \tag{1}\]  
where \(L=l_{\rm s}/y_{1}\) and \(\rm S=s/y_{1}\) denote the relative length and height of the stilling basin, respectively; \(a\), \(b\), \(c\), and \(d\) are constants. The relative first and second pressure peak values for sill configurations can be expressed with the following equations. \[\frac{H_{p1}}{s}=2.165F_{1}^{0.970}L^{0.182}S^{-0.718}, \tag{2}\]  
\[\frac{H_{p2}}{s}=3.010F_{1}^{0.346}L^{0.204}S^{-0.506}, \tag{3}\]  
The graphs of Equations (2) and (3) are plotted in Figure 9. Equations (2) and (3) also reflected the relationships among the peak pressure heads, inflow conditions, and the sill configurations; i.e., the relative peak pressure increased with an increasing \(F_{1}\) and the relative length of the silling basin \(l_{\rm s}/y_{1}\) but decreased with increasing sill height \(s/y_{1}\).
# Chosen Plaintext Combined Attack against SM4 Algorithm
### Contributions  
In this paper, we propose a new round-reduced chosen plaintext power analysis against SM4 which combines chosen plaintext attack and differential analysis. After two rounds of analysis, the initial 128-bit key of SM4 can be completely recovered. Compared to the traditional chosen plaintext attacks [13; 14; 15; 16], our attack has the following advantages:1. Our attack can recover two round keys in one round of analysis simultaneously. For the previous chosen plaintext power analysis, only one round key can be recovered in one round of analysis and requires the analysis of rounds 1-4 in total. However, in our attack, only the S-box outputs of round 2 (or 4) are selected as the attack intermediate values to carry out the chosen plaintext attack by inputting special plaintexts. It can determine some fixed value about the first and second round keys (or the 3 rd and 4 th round keys). Then, by employing the differential characteristics of S-box, we can further determine \(2^{4}\) candidates for the two round keys with near 100% probability in one round of analysis.
2. Our attack is more feasible and simpler for experiments. As mentioned above, our attack reduces the rounds of analysis. Correspondingly, we just need to collect power traces for twice, while the traditional attacks need 4 times. Furthermore, if we improve the method (see Section 3.3), i.e., guess all the \(2^{4}\) candidates of round keys derived by differential analysis and recalculate the correlation coefficients to distinguish the correct ones, the required number of traces will decrease by one third and the key search space complexity will be reduced. This makes the attack experiments more feasible.
3. The target selected in our attack has stronger power leakage. All of the previous attacks targeted the linear operations such as the XOR operation before a round outputting as the leaked points, but our attack targets the nonlinear operation, i.e., the output of S-box. Under the same and unprotected implementation, the leakage of the S-box is obviously greater than the linear operations. This means our attack experiments can succeed more easily due to the stronger power leakage.
## 4 Conclusions  
The temperature dependence performances of MWIR Ga-free T2 SL barrier photodetector have been reported.  
At the expected temperature operation (T = 150 K), the quantum efficiency of the device, with no ARC, as high as 50% was achieved, but it is necessary to apply \(-\)400 mV (Vop) to reach this QE value. This means that there is a valence band barrier blocking the minority heavy hole carriers remaining at the AL/BL interface. A dark current density of the device as low as 3.5 \(\times\) 10\({}^{-5}\) A/cm\({}^{2}\) was recorded. This result is on par with the state of the art, but the Arrhenius plot extracted from J-V measurements as a function of temperature shows that at temperatures lower than 150 K, the device is GR-limited, evidencing the presence of an unwanted electric field in the T2 SL AL. Such a presence is consistent with C-V measurements showing higher residual doping in the BL.  
The results obtained in this study highlight that the main problems are the presence of the electric field in the AL and a valence band offset at the BL-AL interface impeding the flow of minority carriers. To overcome these problems, a study of the AlAsSb BL must be conducted in terms of doping, thickness layer and alloy composition. This will be the subject of forthcoming investigations.  
[PERSON] and [PERSON] fabricated the structures and devices; [PERSON], [PERSON] and [PERSON] performed the measurements; [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] analyzed the data; [PERSON] and [PERSON] wrote the paper. All authors have read and agreed to the published version of the manuscript.  
This work was partially funded by the French \"Investment for the Future\" program (EquipEx EXTRA, ANR 11-EQPX-0016) and by the French ANR under project HOT-MWIR (ANR-18-CE24-0019-01).  
Not applicable.  
The data that support the findings of this study are available from the corresponding author upon reasonable request.  
The authors declare no conflict of interest.  
Figure 10: The 1/C\({}^{2}\)(V) characteristic extracted from C-V measurement at T = 150 K.
## 3 Performance Evaluation and Discussion
### Comparison  
To further evaluate the accuracy of the proposed method, we compare our algorithm with the algorithm proposed in [1] by using the same dataset, i.e., the metro tunnel in Shanghai, China. The Shanghai dataset is described in Table 2. From the Shanghai dataset, we extract 260 tunnel profiles at the fixed split interval of 0.2 m and compare the variation of the fitted radii with the design radius of 2.75 m. Note that the design radii of the tunnels in the Nanjing and Shanghai datasets are both 2.75 m. The detailed statistics for the radii are shown in Table 3. From this table, the radius difference, i.e., RMSE between the cross sections and the designed value is only 4.6 mm, which outperforms the value of 6 mm estimated in [PERSON]'s method [1].  
Compared with the Nanjing dataset, the range of the zenith angle for acquiring the Shanghai dataset is relatively narrow, causing very severe missing data at the top and bottom of scanner locations, as evident in Figure 15a,b. Although we suffer from very serious missing data, our algorithm can infer the accurate locations of cross section points in these missing data areas by an assumption of circular tunnel shapes, as proved in Figure 15d,e. Moreover, the Shanghai dataset used in our paper only includes one scan data without overlapping point clouds from other scans. Therefore, the point clouds that are far away from the scanner have low point density. As shown in Figure 15c, the distribution of point clouds from one terminal is very sparse. However, in this case, our algorithm is still insensitive to this adverse situation and has the capability to acquire the credible cross sections (see Figure 15f). This is mainly due to the adaption of \"self-adaptation\" strategy, which dynamically determines the size of local points to finalize the local quadric surface fitting according to point density variation.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Scanner** & **Scan Angular Resolution** & **\#Pts** & **Range Accuracy** \\ \hline RIEGL VZ-400 & 0.046\({}^{\circ}\) & 2,686,866 & \(\pm\) 5 mm \\ \hline \hline \end{tabular}
\end{table}
Table 2: Description of Shanghai subway tunnel dataset. #Pts represents the number of points of the Shanghai metro tunnel scan dataset.  
Figure 14: The comparisons of those generated and the reference tunnel model at different \(\Delta\varphi\) and \(\Delta d\).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**\#Profile** & _Max_ & _Min_ & **Mean** & \(\mathcal{SD}\) & **RMSE** \\ \hline
260 & 2.7632 m & 2.7481 m & 2.7538 m & 0.0026 m & 0.0046 m \\ \hline \hline \end{tabular}
\end{table}
Table 3: Tunnel radius statistics for 260 cross sections from one scan of Shanghai dataset. #Profile represents the number of the extracted cross sections from Shanghai dataset.