esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Optimized LC-MS/MS Conditions  
For LC-MS/MS, chromatographic separation was performed on a Waters ACQUITY ultra-performance liquid chromatography coupled with a Xevo TQ-XS (Waters, Milford, MA, USA) system. The Waters ACQUITY UPLC BEH C\({}_{18}\) (2.1 mm \(\times\) 100 mm, 1.7 \(\upmu\)m; Waters, Milford, MA, USA) column was maintained at 40 \({}^{\circ}\)C. The mobile phase consisted of (A) 5 mM ammonium acetate in water containing 0.1% formic acid and (B) ACN-MeOH (80:20, \(\it{v}\)/\(\it{v}\)). The flow rate and injection volume equaled 0.3 mL/min and 2 \(\upmu\)L, respectively. The gradient was as follows: 0-4 min (B: 5-40%), 4-6 min (B: 40-70%), 6-8 min (B: 70-95%), 8-10 min (B: 95%), 10-10.1 min (B: 95-100%), 10.1-12 min (B: 100%), 12-14 min (B: 100-5%), and 14-17 min (B: 5%). Electrospray ionization (ESI) was operated in both positive- and negative-ion modes. The other MS parameters were as follows: capillary voltage = 2.5 kV, desolvation temperature = 400 \({}^{\circ}\)C, desolvation gas (N\({}_{2}\)) flow = 800 L/h, and cone gas (N\({}_{2}\)) flow = 150 L/h.
## 2 Materials and Methods
### BHT Data Filtering and Calculating Thermal Gradient  
First, all points that did not include both BHT and TVD data were removed. The average thermal gradient (\({}^{\circ}\)C/km) from the surface for each data point was calculated using Equation (1):  
\[\text{Thermal Gradient}=1000\times\frac{(\text{BHT}-\text{ST})}{\text{TVD}}, \tag{1}\]  
where BHT is bottomhole temperature in \({}^{\circ}\)C, ST is surface temperature in \({}^{\circ}\)C (calculated from mean annual temperature), and TVD is true vertical depth in m. Mean annual temperature of Alberta from 1961 to 1990 was 0.6 \({}^{\circ}\)C [28]. The data were then plotted both by BHT vs. depth and thermal gradient vs. depth.  
Next, the obvious outliers were removed, including wells with unusually high (>39 \({}^{\circ}\)C/km) or low (<20 \({}^{\circ}\)C/km) thermal gradients, because they were not consistent with the assumed conductive heat flow in the area and most of the data. The outliers of anomalously high temperatures at high depths were kept for future research, as it may be valuable to look at each data point to assess the legitimacy of the recording.  
Other obvious outliers included wells where companies reported the same temperature for multiple wells with different depths. Also, temperature measurements for wells <1 km TVD have been shown to be biased, so these data points were removed [23]. From the work of others, individual outliers of high temperatures at greater depths could be Fahrenheit (F) recorded as Celsius (C), and outlier groups at shallow depth could be due to various factors such as incorrect reading or resetting of the maximum reading thermometers (which give anomalously high temperatures) and, occasionally, recording TVD and BHT as the same value [7]. These errors provide insight into the quality of the data and illustrate that great care must be taken to assess the validity of each measurement  
After the filtering process, 1785 data points remained. BHT vs. depth data were plotted then fitted with a linear trendline to calculate the averaged uncorrected thermal gradient. The thermal gradient vs. depth data were also plotted and fitted with a linear trendline to assess the change in thermal gradient with depth.  
The average thermal gradient of these wells was calculated to be 23.9 \({}^{\circ}\)C/km (Figure 4). The average thermal gradient change with depth was calculated to be \(-1.5\)\({}^{\circ}\)C/km, suggesting that gradient does not change significantly with depth. This means that using the gradient to calculate temperature at depths may be straightforward, but caution should be taken when interpreting the results and extrapolating to depth due to uncertainty of the accuracy of the BHT data.
## 4 Methodology
### Designing the DNN-Based Detection Model  
Developing a DNN-based signal detection system for both SISO and MIMO configurations was a critical step in this work. A significant challenge involves determining the SNR values corresponding to various SER levels [14].  
A SISO-based DNN model was designed and trained to receive input signals, process them, and output the detected symbols. The training process utilized a dataset, optimizing the DNN to minimize SER across all SNR levels [15]. Following training, the performance of each method (MLE, MMSE, ZF, and DNN) was evaluated by measuring their SER at various SNRs. This involved feeding the received signals with different SNR levels to both the DNN and the conventional methods. The detected symbols were then compared with the ground truth symbols to calculate the SER [16]. A detailed discussion of the results and a comparative analysis are presented in Section 5.  
Table 1 summarizes the key aspects of the proposed DNN-based signal detection system methodology for both SISO and MIMO configurations, making it easier to understand the training process and network configuration.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Aspect** & **Description** \\ \hline Architecture & 7 hidden layers, each with 100 neurons \\ \hline Activation function (hidden layers) & ReLU \\ \hline
**Training Process** & \\ \hline Dataset preparation & Large dataset of received signals and \\  & corresponding transmitted symbols \\ \hline Train/test split & 70\% training data, 30\% testing data \\ \hline Optimization algorithm & Stochastic gradient descent (SGD) \\ \hline \hline \end{tabular}
\end{table}
Table 1: DNN-based signal detection system methodology.
# A Novel IEEE 802.15.4e DSME MAC for Wireless Sensor Networks
## 1 Introduction
This group acknowledgement option improves the network efficiency as acknowledgement (ACK) for multiple data frames are aggregated to a single ACK frame. However, the devices allocate an additional DSME GTS for Retransmission (GTSR) per each allocated DSME GTS to transmit data to the coordinator. These slots are used for retransmissions of their failed GTS transmissions or for transmitting additional data frames. If a device is succeeded in the first attempt of the GTS and has no new data to transmit in the allocated DSME-GTSR, the allocated slot becomes useless. This problem needs to be addressed so that those DSME-GTSR slots can be reused for other devices. Mobility of devices are imminent in many IoT applications such as habitat monitoring, home automation, smart building, entertainment and patient monitoring etc. Due to mobility, devices frequently leave or enter into the network, by which their synchronization is lost. In order to join a beacon enabled network, devices first discover the coordinator by scanning channels and then join the network by using association messages. The channel scanning durations are defined based on the Beacon Order (BO) of the coordinator. The longer is the BO, the higher is the latency of the coordinator discovery. Again, neither the channel on which the coordinator operates nor its frequency of beaconing is known to a new device that enters into the network. Therefore, minimization of association time for a device with the coordinator is a challenging task, which needs to be addressed.
## 2 Background  
### Pallet Definition and Characterization  
As defined by the SFS-EN ISO 445 standard, pallets are \"rigid horizontal platforms of minimum height, compatible with handling by pallet trucks and forklift trucks and other appropriate handling equipment, used as a base for assembling, loading, storing, handling, stacking, transporting, or displaying goods and loads\" [5]. Approximately 95% of pallets produced are made of wood, sourced from either hardwood or softwood [6]. The advantages of using wood for pallet production include its rigidity, durability, longevity, rapid availability, and relatively low cost [7]. Pallets can be classified into two main categories based on their material and usage:  
* One-way pallets: These are low-cost, low-quality wooden pallets that do not adhere to specific measurement standards. These are non-reusable and are typically discarded after a single use, so there are no requirements for sanitization treatments [8].
* EPAL pallets: These pallets, shown in Figure 2, are certified, high-quality pallets often referred to as'standard pallets' because they must comply with European regulations governing their dimensions, weight, and hygiene. EPAL pallets are made from 11 quality timber boards, nine woodchip or solid wood blocks, and 78 nails. These pallets primarily use wood from sustainable sources and are treated with fumigation processes to enhance their strength and hygiene. EPAL pallets are identified by a stamp marked on the side block.  
As certified by the European Pallet Association, over 650 million EPAL pallets are in circulation worldwide, playing a crucial role in the industry and logistics sector [9].  
* Plastic pallets: These are certified according to European and international standards; plastic pallets are often chosen for their light weight, a potentially indefinite lifecycle, and guaranteed hygiene and safety [10,11]. Unlike wooden pallets, they lack splinters and nails.  
This study was carried out using the standard EPAL pallet, a non-reversible, four-way flat pallet weighing 25 kg. It has a safe working load of 1500 kg, and its dimensions are 1200 mm and 800 mm.  
Figure 2: EPAL pallet specifications.
# Machine-Learning Application for a Likelihood Ratio Estimation Problem at LHC
## 1 Introduction  
The Large Hadron Collider (LHC) [1] is a proton-proton particle accelerator placed at CERN whose physics program, after the successful Higgs boson discovery in 2012 [2], aims to search for new phenomena in physics. The LHC long-time schedule involves the collection of a considerable amount of data both to improve the precision of measurements and to find any extremely rare phenomena predicted by Beyond Standard Model theories. This goal can be achieved by increasing the number of proton collisions within the accelerator ring; however, to be able to collect data at a higher frequency, it was necessary to implement a complex upgrade program, on the hardware, electronics and software. The capacity to process and analyse the large amount of data is essential. In this context, machine learning (ML) finds fertile ground for application, as it offers solutions to the need for rapid analysis of inner structures and hidden pieces of information in large datasets.  
In this paper, we focus on one of the many different applications of an ML algorithm for particle physics analysis, involving the adoption of a deep neural network to address one of the main challenges, that of background process estimation. Techniques applied have mostly involved Monte Carlo simulations, but, if the complexity of the processes is too high, a data-driven approach is preferred.  
The application examples considered are drawn from two publications of the ATLAS Collaboration [3], one of the four major experiments designed to analyze data from particle collisions, involving a broad physics program ranging from precision measurements to direct searches for new particles and new interactions.  
In Section 2, the physical problem is introduced and the theoretical solution adopted is described. Two applications to specific analyses are then discussed in Section 3.
# DPMS: Data-Driven Promotional Management System of Universities Using Deep Learning on Social Media
## 3 Methodology
### Model Selection and Architecture
#### 3.3.2 Network Architecture  
The BiLSTM network used in this paper is illustrated in Figure 2. It is a branch of the Recurrent Neural Network (RNN) that has the capability of processing data in both the forward and backward directions [39]. The network has been designed to handle input strings with a maximum of 2000 nodes. After the input layer, there is an embedding layer. This layer is responsible for converting the input into a feature vector. The vectors then enter the bi-directional layer, where the features are extracted from both the forward and backward directions. These features enter into a global max pooling layer. There is a dense layer after it with 128 nodes. The signals from the dense layer go to the output layer. The output layer has four nodes, each of which is processed through the Softmax function to calculate the probability of the likelihood of a certain class.  
The BiLSTM layers have input, forget, and output gates, which are defined by Equations (17), (18), and (19), respectively. Each of these gates is time-dependent. In these equations, \(x_{t}\) is the input to the BiLSTM layer at time \(t\). And the hidden state is defined as \(h_{t}\) at time \(t\). And the output is expressed by \(o_{t}\) at time \(t\).  
\[i_{t} =\sigma(W_{xi}x_{t}+W_{hi}h_{t-1}+b_{i}) \tag{17}\] \[f_{t} =\sigma(W_{xf}x_{t}+W_{hf}h_{t-1}+b_{f})\] (18) \[o_{t} =\sigma(W_{xo}x_{t}+W_{ho}h_{t-1}+b_{o}) \tag{19}\]  
The BiLSTM network stores information in the candidate cell. It is an essential element of the network, which has been calculated using Equation (20). Each candidate cell changes state when it receives input. The state of the candidate cells has been calculated using Equation (21). The weight matrix of the cell is \(w\) and the bias is \(b\).  
\[\bar{c}t=\tanh(Wxcx_{t}+W_{hc}h_{t-1}+b_{c}) \tag{20}\]  
\[c_{t}=f_{t}\odot c_{t-1}+i_{t}\odot\bar{c}_{t} \tag{21}\]  
The output is calculated using Equation (22). The element-wise multiplication operation is performed to obtain the output, where \(\sigma\) is the Sigmoid function and \(\odot\) means the element-wise multiplications.  
\[h_{t}=o_{t}\odot\tanh(c_{t}) \tag{22}\]  
One of the reasons behind using the BiLSTM network is to utilize its capability of both forward and backward propagation to extract learnable features. The forward process is the sequence in a progressive direction, while the backward process does the opposite. The working principle is the same except for their direction, which is defined by Equation (23).  
Here, \(h_{t}^{(f)}\) is the output produced by the forward processing, and \(h_{t}^{(b)}\) is the output for backward processing.  
\[y_{t}=[h_{t}^{(f)},h_{t}^{(b)}] \tag{23}\]
# Calibrating the SAR SSH of Sentinel-3A and CryoSat-2 over the Corsica Facilities
###### Abstract  
Initially developed to monitor the performance of TOPEX/Poseidon and to follow the Jason legacy satellite altimeters at Senetosa Cape, Corsica, this calibration/validation site has been extended to include a new location at Ajaccio. This addition enables the site to monitor Envisat and ERS missions, CryoSat-2 and, more recently, the SARAL/AltiKa mission and Sentinel-3A satellites. Sentinel-3A and CryoSat-2 carry altimeters that use a synthetic aperture radar (SAR) mode that is different to the conventional pulse-bandwidth limited altimeters often termed \"low resolution mode\" (LRM). The aim of this study is to characterize the sea surface height (SSH) bias of the new SAR altimeter instruments and to demonstrate the improvement of data quality close to the coast. Moreover, some passes of Sentinel-3A and CryoSat-2 overfly both Senetosa and Ajaccio with only a few seconds time difference, allowing us to evaluate the reliability and homogeneity of both ground sites in term of geodetic datum. The Sentinel-3A and CryoSat-2 SSH biases for the SAR mode are respectively +22 \(\pm\) 7 mm and \(-\)73 \(\pm\) 5 mm (for CryoSat-2 baseline C products). The results show that the stability of the SAR SSH bias time series is better than standard LRM altimetry. Moreover, compared to standard LRM data, for which the measurements closer than \(\sim\)10 km from the coast were generally unusable, SAR mode altimeters provide measurements that are reliable at less than few hundred meters from the coast.  
altimetry; SAR; calibration; validation +
Footnote †: journal: _Article_  
Calibrating the SAR SSH of Sentinel-3A and CryoSat-2 over the Corsica Facilities
## 7 Discussion  
The objective and subjective image quality analyses performed in this study provide insight into the effectiveness of the presented algorithm _EEDC-BSSP_ compared to the two standardized image compression methods, _JPEG_ and _JPEG2000_. The four test images (_horse_, _beauty_, _mask_, and _pills_) were compressed at five different compression ratios ranging from 0.8 (lowest) to 0.05 (highest). The results for each image are presented in an image box allowing for a clear visualization of the quality loss of each image.  
The objective image analysis was performed using several metrics, including AAE, MSE, SNR, PSNR, SSIM, MS-SSIM, and VIM. These metrics were presented in tables and used to evaluate the performance of each compression method for the four test images. The objective image analysis showed that EEDC-BSSP outperformed JPEG and JPEG2000 in terms of image quality at high compression rates.  
The subjective image analysis was performed by calculating the MOS for each image using the double stimulus impairment method. The values of MOS were presented in tables and a graph, which showed a clear comparison of the performance of the three compression methods. The subjective analysis confirmed the superiority of EEDC-BSSP over JPEG and JPEG2000 at high compression ratios, as evidenced by the higher values of MOS.  
Overall, the objective and subjective analyzes show that the presented algorithm EEDC-BSSP is more effective in preserving the image quality than the two standardized compression methods evaluated in this study. The results of this study could have important implications for applications that require high compression ratios without a significant loss of image quality, such as medical imaging and satellite imagery. Future work could include further optimization of the EEDC-BSSP algorithm to improve its performance for specific applications.
## 3 Adaptive TrimTree Mechanism
### Rate Adaption Module  
The Rate Adaption module incorporates link rate adaption in all the active Ethernet switches of the DCN. It is done by making them adjust their link rates proportional to buffer occupancy and link utilization. It has been reported that operating switches at lower data rates results in considerable energy saving especially when the network size is large [2; 13]. It has also been observed that a simple  
Figure 3: (a) Fat-tree (b) ElasticTree (MST) (c) Adaptive TrimTree: active + passive network + Rate Adaption.  
policy based on output buffer queue length threshold and utilization monitoring is quite effective in decision-making regarding the adjustment of data link rates [13]. In order to keep our mechanism simple and fast, we incorporate a simple link rate adaption policy adopted by all switches of the 'active network subset'. Queue threshold and link utilization are selected as the parameters for triggering low-to-high and high-to-low data rate transitions respectively. If link data rate is low and the queue length is less than the high queue threshold (around 50% of buffer size) the data rate remains low but if queue length exceeds the high queue threshold, the link rate is set high. This is accomplished by a rate transition Medium Access Control (MAC) frame acknowledged by the receiving end. If the link data rate is high and the queue length is less than the low queue threshold (around 20% of buffer size) and link utilization is less than link utilization threshold, the link data rate is set low. This is accomplished by a rate transition MAC frame sent and if acknowledged, data rate is set to low. Link utilization is monitored by counting bytes sent during a specified period.
## 3 Methodology
### Estimation of Individual Tree Features
#### 3.2.2 Estimation of the Crown Diameter and Crown Volume  
As described under section 2.1 the irregularly shaped crown regions were transformed into circles. Each circle has the same center and the same size as the original crown segment. The diameter of each circle was used as an estimate for _cd_.  
For the estimation of the crown volume _cv_ it was necessary to estimate the crown base height _cbh_ of each tree. For this purpose a procedure for canopy layer detection as suggested by [7] was employed. First, all ALS reflections within a crown segment were selected and \"normalized\" by subtracting the ground surface height. From the normalized points a height distribution function was derived. An example is shown in Figure 5 where the blue curve shows the height distribution of all laser reflections. Many reflections can be found near to the ground and also within the interval from approximately 19 to 27 m. The assumption is that this interval represents the potential crown height. To estimate the crown base height the one-dimensional function was smoothed with a Gaussian function as visualized by the red curve in Figure 5. To determine the height interval that describes the tree crown best, the second derivative of the smoothed function was computed. The result is shown by the green curve. The zero-crossings of the second derivative were analyzed and used to determine the potential crown base height.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Height percentiles–** & **Correlation** & **Height percentiles–** & **Correlation** \\
**all laser points** & **coefficient (r)** & **only first echo points** & **coefficient (r)** \\ \hline _ahp\({}_{max}\)_ & 0,957** & _fhp\({}_{max}\)_ & 0,957** \\ _ahp\({}_{90}\)_ & 0,952** & _fhp90_ & 0,953** \\ _ahp\({}_{80}\)_ & 0,949** & _fhp80_ & 0,949** \\ _ahp\({}_{70}\)_ & 0,944** & _fhp70_ & 0,947** \\ _ahp\({}_{60}\)_ & 0,944** & _fhp60_ & 0,943** \\ _ahp\({}_{50}\)_ & 0,880** & _fhp50_ & 0,942** \\ \hline \end{tabular}  
**\({}^{**}\)** Correlation is significant at the 0.01 level (2-tailed).  
\end{table}
Table 6: Correlations of different height percentiles derived from the ALS point cloud with tree heights measured in the field.  
Finally, the crown volume \(cv\) was estimated for each tree using the normalized DSM and the estimated crown base height \(cbh\):  
\[cv=\underset{(x,y)\in cr}{\sum}(nDSM_{xy}-cbh)\cdot(\Delta x\cdot\Delta y) \tag{1}\]  
where \(cv=\) crown volume [m\({}^{3}\)];  
\(cr=\) crown region;  
\(nDSM_{xy}=\) Height values [m] of the nDSM for each xy position of the crown segment;  
\(cbh=\) crown base height for an individual tree [m];  
\(\Delta x=\) raster width in \(x\) direction [m];  
\(\Delta y=\) raster width in \(y\) direction [m].