esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Analysis of Theoretical Aspects Influencing the System Choice  
Basic data of a warehouse, for which a manipulator has to be designed, are quantified by technical factors and storage possibilities of the given plant. The kind and amount of stored material and warehouse capacity are the main input quantities. In this research, main attention is focused on the infrastructure in term of communications, building layout as well as fire protection. Besides that, we have to take into account existing storage facilities, such as racks, conveyors, further auxiliary means, i.e., pallets, binding and gripping means [8; 9; 10], technical means, i.e., electric and electronic elements, compressed air distribution and others. The main criteria, which were taken into account in the process of the proper system choice, are technical and ecological safety, economy, reliability, life cycle, demands on operation, and affordability. When all the described parameters were accepted, the use of a forklift truck seemed to be the easiest form of manipulation of material. However, due to the character of the transported material, it must not be exposed to exhalants produced by a conventional combustion engine regardless of whether they are gasoline, gas [11; 12], diesel [13; 14] engines or hybrids [15]. Although there are forklift trucks produced with an electric drivetrain [16; 17; 18], in consideration of its acquisition costs and its minimal other use, the purchase of an electric forklift truck was refused. Moreover, the parameters of the transported material would lead to a dangerous situation from the dynamic properties point of view.  
Thus, if we do not consider warehouses in which pallets are transported by means of forklift trucks, we have to focus on such warehouses in which material is handled by other devices. These devices are generally automatised and controlled by computers [19; 20; 21]. However, such a solution suits a warehouse with large capacity and mainly for large assortment, where it is possible to easily reach every one of them. Very narrow aisle trucks, which move in the aisle of a warehouse fully meet that requirement [22; 23]. The use of a transelevator [24] was assessed as unprofitable, and therefore it was not considered. An implementation of a shelf stacker was not rejected, because this solution satisfies demands on one hand in terms of the geometrical parameters of the warehouse and on the other hand in terms of the special environment of the warehouse (humidity, temperature and purity) as well as in terms of the price. Figure 2 shows a partial three-dimensional view of the workplace, for which the manipulator is designed. It is obvious, that neither a common elevator cannot be  
Figure 1: A three-dimensional model of the design of the created platform manipulator.  
installed in the warehouse, because its structure would cause as an obstacle to the emergency exit. After consideration and acceptance of all facts described above, the solution of an oblique transport of material by means of the platform was suggested. Such a solution includes all needed properties and meets all operational conditions.
## 5 Barriers to Internet Access for Women in Developing Countries
### Social Norms Favouring Men
Moreover, research [30] indicates that tele-centres in Sri Lanka have been less than successful due to a combination of factors, including cost, logistics, scarcity of resources, connectivity speeds and lack of support.
# Review of Recent Advances in QEPAS-Based Trace Gas Sensing
## 4 QEPAS Sensor Based on Custom QTF
### QEPAS Sensor Employing a Custom QTF with a Large Prong Gap  
When excitation sources with bad beam quality such as light-emitting diodes (LEDs), vertical cavity surface emitting lasers (VCSELs), and terahertz (Thz) QCLs are used in QEPAS sensors, the large beam diameter and divergence angle make the light beam unable to pass through the micro-resonators and the 300 \(\upmu\)m gap between the two prongs of usually used standard QTF totally, resulting in obvious optical noise. Custom QTFs with a large prong gap can solve this issue [67; 68; 69]. The first custom-made QTF employed in a QEPAS sensor combining a 3.93 THz QCL was reported by [PERSON] [70]. The gap between the two QTF prongs was 1 mm. The fundamental resonance frequency and Q factor at atmospheric pressure of this custom QTF were 4246 Hz and 9930, respectively. The comparison of parameters of geometries for a standard QTF and the custom QTF is shown in Table 1. Methanol detection was performed to verify the performance of this THz-QEPAS sensor with custom QTF. The obtained detection limit was 7 ppm at 4 s integration time and the corresponding NNEA was 2 \(\times\) 10\({}^{-10}\) cm\({}^{-1}\)W/\(\sqrt{\rm{Hz}}\).
# Indoor MIMO-VLC Using Angle Diversity Transmitters
## 3 MIMO-VLC Using ADTs
### LOS Channel Gain Using ADT
Moreover, given the positions of the \(n\)-th side LED in the \(m\)-th ADT and the \(i\)-th PD, the vector \(\mathbf{v}_{i,mn}\) is obtained by  
\[\mathbf{v}_{i,mn}=(\hat{x}_{i}-x_{mn},\hat{y}_{i}-y_{mn},\hat{z}_{i}-z_{mn}). \tag{11}\]  
Substituting (10) and (11) into (9), we have:  
\[\cos\alpha_{i,mn}=\] \[\frac{\left[\left(\hat{x}_{i}-x_{mn}\right)\cos\omega_{n}+\left( \hat{y}_{i}-y_{mn}\right)\sin\omega_{n}\right]\sin\theta-\left(\hat{z}_{i}-z_{ mn}\right)\cos\theta}{\left[\left(\hat{x}_{i}-x_{mn}\right)^{2}+\left(\hat{y}_{i}-y_{ mn}\right)^{2}+\left(\hat{z}_{i}-z_{mn}\right)^{2}\right]^{1/2}}. \tag{12}\]  
Finally, the LOS channel gain between the \(n\)-th side LED in the \(m\)-th ADT and the \(i\)-th PD, i.e., \(h_{i,mn}\) (\(n=1,\ldots,N-1\)), can be successfully calculated by substituting (8) and (12) into (1). Since all the LEDs in each ADT transmit the same signal, the overall LOS channel gain between the \(m\)-th ADT and the \(i\)-th PD is contributed by both the center LED and the \(N-1\) side LEDs, which can be given as follows:  
\[h_{i,m}=h_{i,m0}+\sum_{n=1}^{N-1}h_{i,mn}. \tag{13}\]
## 2 Materials and Methods
### Statistical Analysis  
All participants were assessed for their hydration level at the beginning of each experimental session. One-way ANOVA was used to compare the USG mean scores. To determine the effect that mouth rinsing and ingesting PJ had on cramp duration, EMG raw data were rectified and digitised, and then individual values for PJMR and PJI were normalised to the water condition before being analysed. The EMG data was notch filtered at 50 Hz using a 2 nd order zero lag Butterworth filter. The data were then smoothed using a moving root mean square window of 1 s. The minimum of these smoothed data was located, and the mean of the surrounding 0.5 s of smoothed data was calculated. The time point at which the smoothed data dropped below 1.5x the minimum smoothed value for 0.1s was then located to determine the offset time of the cramp.  
Linear mixed models with random intercept participant effects were used to compare the VAS between conditions over time, and the results were summarised using a graphical representation of the marginal estimates. The time to cramp-end and time to zero discomfort (VAS = 0) between conditions were estimated using Kaplan-Meier survival probabilities. Results were summarised using Kaplan-Meier curves and medians and 95% confidence intervals of times to events. Log Rank tests were used to compare the survival probabilities between conditions. The effect of the condition on the time to zero discomfort was examined using Cox proportional hazards regression, and the effects were summarised using hazard ratios and 95% confidence intervals. The participants were censored if the VAS did not reach zero. Longitudinal and mixed model data were analysed using Stata version 15.1 (StataCorp, College Station, TX, USA); all other data were analysed with IBM SPSS version 24.0 (IBM Corporation, New York, NY, USA). Statistical significance was accepted at \(p<0.05\).
# Overexpression of _RrGGP2_ and _RrDHAR_ Increases Ascorbic Acid Content in Tomato
## 3 Results
### Correlation Analysis between Gene Expression and AsA Content  
#### 3.4.1 Correlation Analysis between Gene Expression and AsA Content in Transgenic Tomato Fruits  
There was a significant correlation between the expression of _SIGGP_ and the content of AsA in the fruits of tomato lines overexpressing _RrGGP2_; additionally, positive correlations were observed with _SIPMI_, _SIGME_, and _SIDHAR_. The expression of _SIAO_ was significantly correlated with DHA (Table 2). In the _RrDHAR_ overexpressing tomato lines, _SIGME_ was significantly correlated with AsA content, while _SIGMP_ and _SIDHAR_ expression were also highly correlated with AsA levels. Furthermore, _SIAO_ was observed to have a significant correlation with DHA (Table 2).  
\begin{table}
\begin{tabular}{l c c c c c c c c c c c c c} \hline \hline  & & \multicolumn{6}{c}{**Correlation Coefficient**} \\ \hline  & & **CPH** & **PMI** & **PMM** & **GMP** & **CME** & **GGP** & **GPP** & **CDH** & **GLDH** & **AO** & **APX** & **DHAR** & **MDHAR** \\ \hline _RrGGP2_ & AsA & \(-0.802\) & \(0.739\) & \(-0.328\) & \(-0.854\) & \(0.521\) & \(0.951\) & \(-0.938\) & \(0.171\) & \(-0.574\) & \(0.270\) & \(-0.416\) & \(0.848\) & \(0.603\) \\  & DHA & \(-0.270\) & \(0.228\) & \(0.030\) & \(-0.708\) & \(0.361\) & \(0.097\) & \(-0.55\) & \(0.617\) & \(-0.500\) & \(0.953\) & \(-0.699\) & \(0.445\) & \(0.697\) \\ \hline \multirow{3}{*}{_RrDHAR_} & AsA & \(-0.789\) & \(-0.826\) & \(0.485\) & \(0.878\) & \(0.965\) & \(-0.664\) & \(-0.433\) & \(-0.397\) & \(0.505\) & \(0.481\) & \(0.634\) & \(0.83\) & \(0.677\) \\  & DHA & \(-0.806\) & \(-0.526\) & \(0.636\) & \(0.762\) & \(0.540\) & \(-0.447\) & \(-0.600\) & \(-0.637\) & \(0.444\) & \(0.910\) & \(0.819\) & \(0.874\) & \(0.819\) \\ \hline \end{tabular}
\end{table}
Table 2: Correlation analysis between gene expression and AsA content in transgenic tomato fruits.  
Figure 4: Relative expression of AsA biosynthesis, metabolism, and recycling-related genes in the WT and transgenic lines. (**A**) Expression of transgenic _RrGGP2_ fruits; (**B**) expression of transgenic _RrDHAR_ fruits; (**C**) expression of transgenic _RrGGP2_ leaves; (**D**) expression of transgenic _RrDAHR_ leaves. The relative expression levels of each gene were obtained using the comparative Ct (2\({}^{-\Delta\Delta\text{Ct}}\)) method; three replicate experiments were performed; error bars represent standard error, means \(\pm\) SE; the same letter above bars indicates a nonsignificant difference at the \(p\) \(<\) 0.05 probability level.
# Quantum Time-Memory-Data Tradeoff Attacks on the SNOW-V Stream Cipher
## 4 TMDTO Attacks on SNOW-V with BSW Sampling
### Sampling Resistances of SNOW-V  
Recalling the description of SNOW-V, we can rewrite the update functions of two LFSRs to facilitate the calculation of sampling resistances.  
\[\begin{array}{cc}LFSR-A:&T1^{(t-1)},T1^{(t)},T2^{(t+1)}\Rightarrow T2^{(t+2)} \\ LFSR-B:&T2^{(t)},T1^{(t-1)},T1^{(t)}\Rightarrow T1^{(t+1)}\end{array}\]  
where  
\[\begin{array}{c}T1^{(t-1)}=\left(b_{15}^{(8t-8)},\cdots,b_{8}^{(8t-8)}\right) =\left(b_{7}^{(8t)},\cdots,b_{0}^{(8t)}\right)\\ T1^{(t)}=\left(b_{15}^{(8t)},\cdots,b_{8}^{(8t)}\right)\\ T1^{(t+1)}=\left(b_{15}^{(8t+8)},\cdots,b_{8}^{(8t+8)}\right)\\ \end{array}\]  
\[\begin{array}{c}T2^{(t)}=\left(a_{7}^{(8t)},\cdots,a_{0}^{(8t)}\right)\\ T2^{(t+1)}=\left(a_{7}^{(8t+8)},\cdots,a_{0}^{(8t+8)}\right)=\left(a_{15}^{(8t) },\cdots,a_{8}^{(8t)}\right)\\ T2^{(t+2)}=\left(a_{7}^{(8t+16)},\cdots,a_{0}^{(8t+16)}\right)=\left(a_{15}^{(8 t+8)},\cdots,a_{8}^{(8t+8)}\right)\end{array}\]  
Recalling Assumption 1 above, it is easy to verify that \(l~{}=~{}128\) and \(R~{}=~{}2^{-128}\) satisfy for SNOW-V. The attacker can guess \(R1^{(t)},R3^{(t)},T1^{(t)},T1^{(t-1)},T2^{(t)},T2^{(t+1)}\) and, then, recover \(R2^{(t)}\) by \(z^{(t)}=\left(R1^{(t)}\boxplus_{32}T1^{(t)}\right)\oplus R2^{(t)}\). This means that the attacker can determine \(128\) unknown internal state bits (i.e., \(R2^{(t)}\)) using \(128\) keystream bits (i.e., \(z^{(t)}\)), after guessing \(896-128~{}=~{}768\) internal state bits (i.e., \(R1^{(t)},R3^{(t)},T1^{(t)},T1^{(t-1)},T2^{(t)},T2^{(t+1)}\)). Hence, \(l\)=\(128\) and \(R~{}=~{}2^{-128}\) satisfy for SNOW-V.  
Similarly, the attacker can guess \(R1^{(t)},T1^{(t)},T1^{(t-1)},T2^{(t)},T2^{(t+1)}\) and, then, execute the following process to recover \(R2^{(t)}\) and \(R3^{(t)}\).  
* Determine \(R2^{(t)}\) by \(z^{(t)}=\left(R1^{(t)}\boxplus_{32}T1^{(t)}\right)\oplus R2^{(t)}\).
* Determine \(R1^{(t-1)}\) by \(R2^{(t)}=AES^{R}\left(R1^{(t-1)},C1\right)\).
* Determine \(R2^{(t-1)}\) by \(z^{(t-1)}=\left(R1^{(t-1)}\boxplus_{32}T1^{(t-1)}\right)\oplus R2^{(t-1)}\).
* Determine \(R3^{(t)}\) by \(R3^{(t)}=AES^{R}\left(R2^{(t-1)},C2\right)\).  
The process above shows that the attacker can determine \(256\) unknown internal state bits (i.e., \(R2^{(t)},R3^{(t)}\)) using \(256\) keystream bits (i.e., \(z^{(t)},z^{(t-1)}\)), after guessing \(896~{}-~{}256~{}=~{}640\) internal state bits (i.e., \(R1^{(t)},T1^{(t)},T1^{(t-1)},T2^{(t)},T2^{(t+1)}\)). Hence, \(l\)=\(256\) and \(R~{}=~{}2^{-256}\) satisfy for SNOW-V.  
In [8], [PERSON] et al. had given a specific Guess and Determine attack on SNOW-V, which confirms that \(l=384\) and \(R~{}=~{}2^{-384}\) satisfy for SNOW-V. In their work, the attacker can guess \(R1^{(t)},R1^{(2)},T1^{(t-1)},T2^{(t)}\) and, then, execute a specific process to recover \(R2^{(t)},R3^{(t)},T1^{(t)},T2^{(t+1)}\) with \(384\) keystream bits \(z^{(t)},z^{(t+1)},z^{(t+2)}\). Hence, \(l~{}=~{}384\) and \(R~{}=~{}2^{-384}\) satisfy for SNOW-V.
## 4 Evaluation
### Simulation on the Components of AcpAS
#### 4.1.2 Simulation on the Optimized Coaxial-to-Waveguide Transition  
The simulation results of the side-fed coaxial-to-waveguide transition are shown in Figure 13. The simulation results indicate that the optimized transition structure has good performance, with return loss being greater than 20 dB in both the K-band and Ka-band and insertion loss being less than 0.1 dB. Figure 13b provides the electric field distribution inside the waveguide.  
Figure 14 shows the simulation results of the performance of the K-band septum polarizer after using the side-fed coaxial-to-waveguide transition, when the probe length varies by \(\pm\)0.2 mm. It can be seen that at this time, the change in probe length has almost no effect on the performance of the septum polarizer. Therefore, by using the side-fed coaxial-to-waveguide transition, the impact of errors in manufacturing or installation on the performance of the septum polarizer can be effectively reduced.
# Captioning Transformer with Stacked Attention Modules
## 4 Experiments  
For evaluating the proposed model, the MSCOCO dataset [12] is used. The MSCOCO dataset contains 82,783 training images, 40,504 validation images and 40,775 testing images. For comparing with other state-of-the-art methods, we use the same dataset splits as in [4]. Nowadays, this split is used as the offline evaluation. This training set of this split contains 113,287 images, 5000 validation images and 5000 testing images. The result of the model is reported on the testing dataset.  
We use CIDEr [13], BLEU [14], METEOR [15] and ROUGE_L metrics to evaluate the quality of the generated sentences. CIDEr [13] measures consensus in image caption by performing a Term Frequency-Inverse Document Frequency weighting for each \(n\)-gram. BLEU [14] is a precision-based metric and it is traditionally used in the machine translation to measure the similarity between the generated captions and ground truth captions. METEOR [15] is based on explicit word to word matches between the generated captions and the ground-truth captions.  
For the Transformer model, we set the model size which is \(d_{model}\) reported in Section 3.4.2 to be 512 and the mini-batch to be 16. We use the Adam method [16] to update the parameters of CNN and the Transformer. The initial learning rate of the Transformer is \(4\times 10^{-4}\), and the initial learning rate of the CNN is \(1\times 10^{-5}\). The momentum and the weight-decay are 0.8 and 0.999 respectively. We utilize the PyTorch deep learning framework to implement our algorithm. In inference, the beam search algorithm is used for better caption generation. In practice, we set the beam size to be 2.  
We adopt the training strategy similar to NIC for the fair comparison. In the process of training Transformer network, we also use the CNN fine-tuning strategy proposed in NIC [1].  
The NIC model and the Soft-Attention model which are based on the LSTM, are our baseline models. In order to maintain the fairness of the comparison, All the models use the same CNN model and hyperparameters. In practice, the number of the Transformer decoder layers is set to be 6, and the attention size used in the model is set to be \(4\times 4\).
# Passive Location for 5G OFDM Radiation Sources Based on Virtual Synthetic Aperture
## 2 Theoretical Analyses of Typical Source Localization Methods
### Frequency Difference of Arrival (FDOA)  
FDOA is rarely used in a location system alone and is usually used in conjunction with the TDOA method. Therefore, FDOA is only briefly described here for the complete analysis. FDOA uses the difference of Doppler frequency shift generated by the relative motion of multiple base stations and sources to obtain the location information of the source. Compared with TDOA, FDOA is more suitable for narrow-band signals, but it is more complex and difficult to solve. Additionally, time and frequency synchronization are highly required [3].  
Table 2 provides a summary and comparison of the localization methods.
# Non-Uniform-Illumination Image Enhancement Algorithm Based on Retinex Theory
## 2 Proposed Method
### HSV Space Conversion  
After applying color balance, the issue of color cast is effectively resolved, laying the groundwork for brightness adjustment. When dealing with RGB images, direct brightness enhancement in the red channel (\(R\)), green channel (\(G\)) and blue channel (\(B\)) will result in image distortion due to excessive color enhancement. To address this concern, the HSV color space proves to be beneficial [17]. It separates the chroma (\(H\)), saturation (\(S\)) and brightness (\(V\)) into three independent channels within the color space, which ensures the balance of each color channel. As a result, the enhancement of the brightness component \(V\) only changes the brightness and darkness of the image. In this paper, the RGB space is converted to the HSV space by the following method [18], specifically expressed as follows:  
\[V=\frac{(R+G+B)}{3} \tag{3}\]  
\[S=\frac{V-MIN(R,G,B)}{V} \tag{4}\]  
\[H=\left\{\begin{array}{ll}60\times(G-B)/(V-MIN(R,G,B))&(if\ V=R)\\ 120+60\times(B-R)/(V-MIN(R,G,B))&(if\ V=R)\\ 240+60\times(R-G)/(V-MIN(R,G,B))&(if\ V=R)\end{array}\right. \tag{5}\]  
where \(H\), \(S\) and \(V\) correspond to hue, saturation and brightness; \(R\), \(G\) and \(B\) represent red, green and blue channels, respectively.
## 1 Introduction  
Acquiring a commercial off-the-shelf (COTS) intrusion detection system (IDS) or network security monitor (NSM) set-up and placing it in an arbitrary location in an industrial control system (ICS) network is a straightforward task in itself. However, customization of the system and rewriting of the various rules is needed before meaningful functionality can be achieved [1]. Device manufacturers and service providers increasingly require remote connections to more of the devices in the field, and therefore industrial automation is slowly merging into the Internet of Things. Despite this, the ICS network environments are still very isolated compared to the traditional ICT organization's networks. Internet connections in the ICS environments should be managed and monitored carefully.  
In the paper [2] we discussed the applicability of the machine learning approach to decrease the amount of manual customization required for deploying NSM system or IDSs in an ICS network.  
After the initial research on the challenges and possibilities of this approach in [2] and sources explained in Section 2, we present a case study on the applicability of machine learning based approaches and possible feature selection for anomaly detection in the particular network environment under investigation. The intention is not to differentiate between generic or targeted attacks in the future, but is currently mainly targeted at more generic IP traffic anomaly detection. The paper is based on a conference paper [3].  
The network traffic captured data on which we based the discussion on the applicability of the features was captured from a live factory environment and is studied in detail in the Section 3.1. The information concerning the nature and location of the industrial site in question as well as the automation equipment in use is withheld. This non-disclosure is due to the requirements by the parties responsible for the site's operation.
# CFD Simulation of the Safety of Unmanned Ship Berthing under the Influence of Various Factors
## 3 Multi-Factor Berthing Safety Study on an Actual Unmanned Ship
### Analysis of the Berthing Characteristics of Actual Unmanned Ship
#### 3.2.3 Analysis of Wind Resistance Characteristics  
The wind characteristics of 0, 45, 90, and 180 degrees at wind speeds of 20 m/s are simulated as shown in Figure 10.  
As can be seen from Figure 10, the front of the unmanned ship is subjected to positive pressure at the wind angle of 0 degrees, with a maximum positive pressure of 261.87 Pa. The arched part of the front edge of the unmanned ship is protruding, the wind speed accelerates when the air is circulating from its upper surface, the pressure is reduced to negative pressure, and the minimum negative pressure is \(-\)466.1 Pa. In addition, the leeward surface of unmanned ships is also subjected to negative pressure, but the pressure reduction is not high. At the 45-degree wind angle, the front wind face of the unmanned  
Figure 9: The bottom pressure characteristics of the unmanned ship at different speeds.  
ship is subjected to positive pressure, and the farther away from the bow, the smaller the positive pressure, the maximum positive pressure is about 307 Pa. On one side of the leeward surface is a negative pressure zone with a minimum value of approximately \(-156\) Pa. The smallest negative pressure appears on the upper surface of the head edge of the windward surface, as shown in the dark blue area of Figure 10, which is about \(-656\) Pa. At a 90-degree wind angle, the wind face is mainly positively pressured, and the closer you get to the hull, the greater the pressure, which is about 263 Pa. The leeward zone is negative pressure, which is about \(-200\) Pa. At the wind angle of 180 degrees, the wind direction is the same as the direction of the ship's navigation, the tail of the unmanned ship is the positive pressure zone, the maximum pressure is about 7.65 Pa, in the tail of the unmanned ship deck and the front of the operator's station is the negative pressure zone, because the wind from the rear of the ship when the wind around the current is larger, the pressure is smaller, forming a negative pressure zone, the minimum pressure is about \(-484\) Pa.