esa-sceva/satcom-chunk-collection huggingface dataset:
# Optimization of Linear Quantization for General and Effective Low Bit-Width Network Compression
## 4 Experiment
### Accuracy Comparison between Different Initialization Methods  
To investigate how each quantization method affects performance, this paper considers three methods to determine the initial centroids of the cluster: random, uniform partition, and the PSO algorithm. The experimental results in Table 4 show that the particle swarm algorithm can effectively optimize the clustering results, and the accuracy is higher and more stable at the low bit-width than the methods using random and uniform partitions of the initial center of the cluster. According to the experimental results, we discover an interesting phenomenon, pruning will not cause the accuracy to decrease. On the contrary,  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Network** & **Bit-Width** & **Centroids** & **Linear** & **K-Means** \\ \hline \multirow{4}{*}{ResNet-18 [33] (69.758)} & 8 & 255 & 69.510 & 69.756 \\  & 7 & 127 & 69.072 & 69.682 \\  & 6 & 63 & 67.290 & 69.494 \\  & 5 & 31 & 53.586 & 68.066 \\  & 4 & 15 & 1.028 & 61.466 \\ \hline \multirow{4}{*}{ResNet-50 [33] (76.13)} & 8 & 255 & 75.868 & 76.054 \\  & 7 & 127 & 75.232 & 76.080 \\ \cline{1-1}  & 6 & 63 & 72.532 & 75.382 \\ \cline{1-1}  & 5 & 31 & 49.278 & 72.242 \\ \cline{1-1}  & 4 & 15 & 0.234 & 67.736 \\ \hline \multirow{4}{*}{DenseNet-121 [34] (74.433)} & 8 & 255 & 74.266 & 74.380 \\  & 7 & 127 & 73.458 & 74.260 \\ \cline{1-1}  & 6 & 63 & 71.308 & 73.644 \\ \cline{1-1}  & 5 & 31 & 56.260 & 72.738 \\ \cline{1-1}  & 4 & 15 & 1.802 & 64.026 \\ \hline \multirow{4}{*}{Inception-v3 [35] (69.538)} & 8 & 255 & 69.968 & 69.478 \\ \cline{1-1}  & 7 & 127 & 67.942 & 69.288 \\ \cline{1-1}  & 6 & 63 & 61.788 & 67.498 \\ \cline{1-1}  & 5 & 31 & 12.892 & 63.414 \\ \cline{1-1}  & 4 & 15 & 0.086 & 20.946 \\ \hline \hline \end{tabular}
\end{table}
Table 3: ImageNet top-1 accuracy with weight clustering—the float accuracy is displayed under the model name. Results include different cluster methods, uniform partition, and K-means.  
when the bit-width is smaller than 5 bits, the accuracy of the model after pruning will be higher than the original model.
## 4 Case Study
### The Basic Test of the Proposed Algorithm
#### 4.1.1 Optimization for Time-Sensitive Appliances  
As mentioned above, lights and water dispensers are two kinds of typical time-sensitive electric appliances. In this section, we chose them to test our energy-saving strategy for time-sensitive appliances. As shown in Figure 6, the running time of appliances greatly reduced, and the energy saving benefits were considerable. It is worth noting that the comfort loss risk for customers can be ensured in the optimization, due to the constraints of Equation (9). For instance, as shown in Figure 6c, the real running hours of the water dispenser (red solid line) reduced sharply after optimization, compared with the original operating hours (blue solid line). However, the value of the utility increased, as shown in Figure 6d. The reason for this is that the operating probability is zero from 0:00 to 8:00 and 18:00 to 24:00, as shown in Figure 5e, which means there was no drinking necessary. As a result, optimizing the operation status in these time slots from heating to insulation will increase the utility value and save more energy, while ensuring customer comfort.  
Figure 5: Utility evaluation of the test electrical appliances. (**a**) Light-power. (**b**) Time demand of lighting. (**c**) Time utility for lighting. (**d**) Water dispenser power. (**e**) Time demand of a water dispenser. (**f**) Time utility of a water dispenser. (**g**) Air conditioning power. (**h**) Indoor temperature. (**i**) Temperature utility of air conditioning.
## 2 Methods
### Graph-Based Design Method
Next, consider the _analysis_ of the solution consisting of two (1 m diameter) 70 m long parallel pipes with specifications similar to \"Case 1\" but with increased airflow rate, i.e.,  
* Pipe burial depth \(z=5.5\) m;
* EAHX inlet air temperature: \(T_{\rm a0}=37\)\({}^{\circ}\)C;
* EAHX airflow rate: \(\dot{V}=16,000\) m\({}^{3}\)/h. This will be referred to as \"Case 2\". Suppose we wish to determine the change to EAHX exit air temperature for this case. Figure 4 confirms two 1 m diameter pipes can be used to deliver a total of 16,000 m\({}^{3}\)/h (8000 m\({}^{3}\)/h per pipe). Point 10 depicts this case with a corresponding airflow velocity of \(\sim\)2.8 m/s. Using this velocity and 1 m diameter, point 11 in Figure 5--upper graph, for fraction \(x/L^{*}=0.575\)--shows that the thermal performance of a (1 m diameter) 70 m long pipe delivering 8000 m\({}^{3}\)/h is equivalent to approximately 60% (the actual value is 57.5%) of the thermal performance of a pipe with the characteristic length \(L^{*}\). From the bottom graph in Figure 3, with \(x/L^{*}=0.575\) and \(T_{\rm a0}-\hat{T}_{\rm so}(z=5.5)=17\) K, it can be seen that the EAHX inlet to outlet air temperature difference is \(T_{\rm a0}-T_{\rm AL}=7.5\) K, which translates into \(T_{\rm aL}=29.5\)\({}^{\circ}\)C.  
Given the larger air flowrate, it is concluded that outdoor air load removal (Equation (1a)) is higher for \"Case 2\"; still, with a larger outflow air temperature, the thermal efficiency (Equation (6)) for \"Case 2\" is lower than for \"Case 1,\" and the proximity to the room setpoint temperature cancels the effect the larger ventilation rate has on room load removal (Equation (1b)). These results provide an important insight into large-diameter EAHX performance and, together with fan energy consumption (the largest EAHX operational cost) and other non-heat-transfer constraints (e.g., excavation and pipe costs, ease of maintenance), contribute to narrowing the set of possible solutions to large-diameter EAHX design problems.
## 2 System Model
### Extraction Algorithm  
A flowchart representation of the proposed extraction algorithm is shown in Figure 8.  
Figure 7: Flowchart representation for display screen extraction.  
The stego images obtained from the display screen extraction process are re-sized to fit the cover image, i.e., 1920 \(\times\) 1080. The key bit stream transmitted by the control room is received and given to a 3-bit splitter. The extraction algorithm extracts the data image with inputs as stego images and keys. The data extraction process steps are given below.  
Step 1: Read the 1920 \(\times\) 1080 stego image obtained from display screen extraction.  
Step 2: Divide the stego image into its three layers red, green and blue.  
Figure 8: Flowchart representation of data extraction algorithm.  
Step 3: Apply 2D-Haar IWT to R-Plane of the stego image and generate LL, LH, HL and HH band.  
Step 4: Select the HH band.  
Step 5: Divide HH band into 4 \(\times\) 4 blocks and select one block at a time randomly using same random number generator used at the transmitter.  
Step 6: The key stream received for the red layer is given to 3-bit splitter, and consecutive 3 LSB bits of the key are selected.  
Step 7: Shift the key to right by 3-bits.  
Step 8: The three bits of the key obtained from Step 6 is used to determine the pattern applied to that block by looking at Table 1.  
Step 9: Extract LSB of integer coefficients based on the selected pattern and create a 4 \(\times\) 4 block in the LL band of black and white binary data image respectively.  
Step 10: Select the next block and repeat from Step 6 to Step 9 until all data blocks of LL band are extracted from the HH band of the R-plane  
Step 11: Obtained LL subband gives black and white binary data image 1(960 \(\times\) 540).  
Step 12: Divide the obtained black and white data image into 4 \(\times\) 4 blocks.  
Step 13: Select the first block and apply mode filter to that block. Mode filter finds the most number of occurrences of '1' and '0' and replaces all the pixels in that block with '1' or '0', whichever is most prevalent.  
Step 14: Select next block in the same row and repeat Step 13 for that block. If the previous block is the last block in that row then, the next block is the first block from the next row.  
Step 15: Repeat Step 14 for all 4 \(\times\) 4 blocks and create restored black and white binary data image (960 \(\times\) 540).  
Step 16: Resize the restored black and white data image (960 \(\times\) 540) into the size of (1920 \(\times\) 1080).  
Step 17: Repeat from Step 3 to Step 16 for green and blue layers using respective received key streams and create resized black and white binary data images.  
Step 18: Extract a secret message from the obtained black and white binary data image.
# _CoVSCode_: A Novel Real-Time Collaborative Programming Environment for Lightweight IDE
## 2 Related Work  
### Real-Time Collaborative Editing  
Similar to many real-time collaborative applications in other domains, one of the fundamental techniques for supporting real-time collaborative programming is the generic real-time collaborative editing technique, which allows a group of collaborators to concurrently edit a shared document at the same time [20; 21]. To achieve high local responsiveness (i.e., for a user's local editing operation to take effect immediately in the local document without delay), real-time collaborative editing systems have commonly been designed with a _replicated architecture_, where the shared document is replicated at all collaborating sites [22]. Consequently, there is an essential consistency maintenance requirement that after all editing operations have been propagated and replayed at remote sites, the shared document at each site must be identical.  
To illustrate the consistency maintenance problem, a simple example is presented in Figure 1 (left side). Suppose that there are two collaborators, namely \(A\) and \(B\), who are editing the shared document at the same time. The document's initial content was \"_abcdef_\". At one moment, \(A\) issues an operation _ins_[0, \"_xy_\"] (to insert the text \"_xy_\" at the position 0 of the document), while at the same time, \(B\) issues an operation _del_[2; 3] (to delete the text \"_cde_\" from the position 2 of the document). After the two operations have arrived at remote sites and been replayed, the evolved documents became inconsistent.  
The illustrated problem has been well investigated within the community of computer-supported cooperative work (CSCW) over the past decades, and can be solved by the _operational transformation_ (OT) technique [20; 23; 24; 25]. In essence, the OT technique is capable of meeting two fundamental consistency requirements (i.e., _convergence_ and _intention-preservation_) in real-time collaborative editing [21; 25]. Supported by the OT technique, the inconsistency problem in the above example can be solved as illustrated in Figure 1 (right side). When the remote operation _del_[2; 3] arrives at \(A\)'s site, it has been transformed into _del_[3; 4] (i.e., the operation's position has increased from 2 to 4). Eventually, the transformed operation correctly deletes the intended text \"_cde_\" from the document, and achieves correct and consistent results.  
It is worth noting that the example presented here is an extremely simple one, whereas numerous cases and issues exist that are far more complex. Fortunately, the CSCW community has made significant efforts in dealing with those problems, and has contributed mature and reliable OT techniques that can be utilized in building real-time collaborative applications [25; 26].
# Use of Machine Learning and Deep Learning to Predict the Outcomes of Major League Baseball Matches
## 3 Methods
### Prediction Model Construction
#### 3.4.2 Ann  
ANNs are some of the most commonly used models for motion prediction in machine learning [30]. A basic ANN comprises an input layer, a hidden layer, and an output layer. The number of hidden layers depend on the complexity of the problem. The higher the number of hidden layers, the slower the learning rate. In [7; 8], one hidden layer was used to construct a model for predicting the outcomes of MLB matches. In this study, because a match result is classified as nonlinear and binary (win/loss), the sigmoid function was used as the excitation function of the neuron in the input layer.  
In this study, an ANN prediction model was constructed in Python. For data set 1 and data set 2, the input variables in the input layer were 30 and 32, respectively, and the number of neurons in the hidden layer was set to 16 and 17, respectively; the output layer was the game win or game loss (1/0). A dropout layer was added after the hidden layer to prevent overfitting; the dropout value was set to 0.1; the kernel_initializer, which is the initialization method, was placed in the input layer and output layer. The initialization methods include Zeros, Ones, RandomNormal, glorot_normal, and he_normal. We used cross-validation in Python along with GridSearchCV to determine the most suitable initialization method for this model; the sigmoid function was set as the activation function for neurons in the output layer.  
The optimizer setting was the same as that in the 1 DCNN. [PERSON] and RMSprop were employed with GridSearchCV to determine the most suitable optimizer for this model. The loss function was set to binary_crossentropy, the batch_size was set to 10, 20, and 30, and the epochs were set to 50, 100, and 150 with fivefold cross-validation.  
Figure 5: 1 DCNN model architecture diagram (take dataset 2 as an example).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Layer (Type)** & **Output Shape** & **Filters** & **Kernel\_Size** & **Strides** \\ \hline Input size & \(32\times 4858\) & - & - & - \\ \hline conv1d\_1 & \(32\times 16\) & \(16\) & \(3\) & \(1\) \\ \hline maxpooling1d\_1 & \(31\times 16\) & - & \(2\) & \(1\) \\ \hline conv1d\_2 & \(31\times 32\) & \(32\) & \(3\) & \(1\) \\ \hline maxpooling1d\_2 & \(15\times 32\) & - & \(2\) & \(1\) \\ \hline Dropout & \(15\times 32\) & - & - & - \\ \hline Flatten & 480 & - & - & - \\ \hline Dense(ReLU) & 50 & - & - & - \\ \hline Dropout & 50 & - & - & - \\ \hline Sigmoid & 1 & - & - & - \\ \hline \hline \end{tabular}
\end{table}
Table 3: 1 DCNN model architecture (take dataset 2 as an example).
# NPS6D200--A Long Range Nanopositioning Stage with 6D Closed Loop Control
## 3 Setup and Subsystems of the 6D Nanopositioning System
### Stage Concept
In addition, Table 1 summarizes the key parameters of the realized drive system. The 6D feedback system comprises four ultra-stable differential interferometers (DI) from SIOS GmbH ([39]) in an arrangement where the measurement beam paths intersect in one point so that it is virtually free of Abbe errors. Interferometers U, V, and W measure \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline _xy_-operating range & \(x\) 200 mm \\ _z_-operating range & 25 mm \\ planar velocity 1  & 50 mm s\({}^{-1}\) \\ vertical velocity & 2 mm s\({}^{-1}\) \\ acceleration & 250 mm s\({}^{-2}\) \\ moving mass & 36 kg \\ payload & 5 kg \\ overall dimensions (W \(\times\) L \(\times\) H) & 1115 mm \(\times\) 980 mm \(\times\) 980 mm \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Key parameters of the positioning system. Figure 1: NPS6D200 main components and general arrangement. Figure 2: Photograph of NPS6D200 long range nanopositioning stage. Components: **1—**slider with reflectors. **2—**magnets of the planar direct drive (moving with the slider). **3—**coils of the planar direct drive (encapsulated) **4—**laser interferometer. **5—**granite portal with probe mount interface. **6—**laser interferometer. **7—**lifting module LAU25. **8—**granite base. the planar motion of the slider in \(x\), \(y\), \(\varphi_{2}\). Note that interferometer U has both beam paths of the DI that lead to the moving reflector, so it directly measures the yaw angle. The pitch between the two beams here is \(\Delta l=14\,\mathrm{mm}\). Together with the distance readings from V and W the coordinate transformation as given in Equation (1) results in \(x\), \(y\) and \(\varphi_{2}\) position of the slider. The vertical DOFs are addressed by the \(z\) interferometer and an autocollimator 2D angle sensor (\(\varphi_{x}\), \(\varphi_{y}\)) which both look at the reflector on the slider's underside. With measurement resolutions as listed in Table 2, this feedback system provides very high sensitivity for all six DOFs and thus forms another important prerequisite for achieving servo errors at the nanometer level with the NPS6D200. \[ \begin{pmatrix}x\\ y\\ \varphi_{z}\end{pmatrix} = \begin{pmatrix}\frac{\sqrt{3}}{2}&-\frac{\sqrt{3}}{2}&0 \\ \frac{1}{2}&\frac{1}{2}&0\\ 0&0&\frac{1}{\Delta l}\end{pmatrix}\begin{pmatrix}\mathrm{V}\\ \mathrm{W}\\ \mathrm{U}\end{pmatrix}  \tag{1}\]  
The fact that all six DOFs are controlled in closed loop ensures that the machine accuracy is independent of manufacturing tolerances or flatness deviations in the guiding system. Even in the rotational degrees of freedom, the position of the slider is not left to passive guidance and its deviations, but is regulated based on the 6D feedback system. This allows disturbances in these DOFs to be compensated, achieving higher positioning accuracy where only the mirror surfaces of the slider define the metrology.
## 2 Methods
### Statistical Analysis  
Three levels of intervention (without Exo--WO; with CEX--\(W_{\text{CEX}}\); and with CC--\(W_{\text{CC}}\)) and three working heights (60, 85, and 110 cm) were selected as independent variables. Muscle activity and subjective discomfort ratings were selected as dependent variables.  
The muscle activity of eight right muscles (four upper-limb muscles and a lower back muscle: upper trapezius, middle deltoid, biceps brachii, triceps brachii, and erect spinae; three lower-limb muscles: rectus femoris, biceps femoris, and tibialis anterior) was measured as dependent variables. Subjective ratings of body discomfort were measured using [PERSON]'s CR-10 scale for six body parts (neck, chest, back, shoulder, arm, and leg) after drilling task performance [22].  
The normality of the dependent variables was examined using Shapiro-Wilk and Kolmogorov-Smirnov tests, and then a generalized linear mixed model (GLMM) was applied for analysis in this study. Then, a post-hoc analysis was performed using [PERSON]'s HSD to compare the differences between interventions and working heights. Subsequently, the interaction effect was also included in this model. All statistical analyses were performed using SPSS 9.0 (SPSS Inc., Chicago, IL, USA). The level of statistical significance was set at \(p<0.05\).  
Figure 2: Drilling task without exoskeleton (WO, **left**), with CEX (\(W_{\text{CEX}}\), **middle**), and with CC (\(W_{\text{CC}}\), **right**) at the working heights of 110 cm (**top**), 85 cm (**middle**), and 60 cm (**bottom**).
## 1 Introduction
We utilized a supernode to identify a one-layer of nodes among supernodes. The nodes placed in the identified layer are called neighbor of supernode (NSN), which can forward the data packet directly to the supernode, reducing retransmission and overall energy consumptions. Moreover, supernodes are responsible for determining the NSN. The one-layer of nodes also reduces networking overheads compared to the architecture with several layers of node identification. On the other hand, in MRP [42], supernodes have been utilized to identify several layers of nodes with different power levels toward the energy centric grouping in the whole network architecture. This is computationally expensive and negatively impacts the energy saving capability with scaled underwater networking scenarios. On the other hand, nodes placed on the bottom of NSN identify its location locally (i.e., depth information), then, the route cost based on residual energy and TM can be calculated. This technique results in overall network performance and greener communication considering higher energy resources and dedicated operation areas without creating communication overheads in the overall underwater network environments [31]. Each supernode is represented as a particular sensor node with unique features such as high energy. It forwards data from sensor nodes to the sink with different power-level transmission. The supernodes are employed at different depth-levels at the top of the progress area to collect the data from ordinary sensor nodes and relay them to the sink. Ordinary nodes forward the data packets to one of the supernodes based on a multi-level depth structure. The significant contributions of this work are as follows. * In addition to supernodes, we propose a new depth-based network architecture for UWSNs. Unlike typical UWSN architectures, the proposed architecture can be used to avert the communication void by utilizing one-layer among supernodes. * Moreover, we also divided TM2 RP based on a multi-level depth distribution structure. Data packets are simply transmitted from the sender toward the sink node according to the depth-level number assigned to each sensor node by supernodes. This approach can reduce the number of hops involved in the forwarding procedure, reduce the total energy consumed, and further improve the network lifetime. * In TM2 RP, data packets are forwarded from the source toward the destination using selected nodes based on depth, link quality, and residual energy. * The proposed architecture is assessed comparatively with state-of-the-art techniques considering energy-centric green computing metrics for underwater networking. The rest of this paper is organized as follows. Section 2 revises some well-known routing algorithms in UWSNs by classifying them into non-localization and localization-based. Section 3 presents TM2 RP. Section 4 addresses the comparative performance evaluation of TM2 RP. Finally, Section 5 concludes the approach proposed in this paper with future research directions.
# Star Type Wireless Sensor Network for Future Distributed Structural Health Monitoring Applications
## 2 Materials and Methods  
### Networking Protocol and Hardware Selection  
Little literature concerning the development of a wireless sensor network capable of measuring angular rotation has been published. There is, however, an extensive catalogue of work concerning the use of accelerometers, both wireless and wired for structural health monitoring [12; 13; 14]. For example, a wireless accelerometer network of Oracle Sun SPOT sensors has been used to assess the structural health of a wind turbine [13]. A device specifically for measuring bell angular rotation has never been developed. The sensor must be wireless as it would be physically attached to a rotating bell wheel in its application. Sending the data via infrared was ruled out because the dusty bell tower environment would have made such communication unreliable. A previous project attempting to solve the same problem with optical sensors proved too impractical to use for research. Group special mobile (GSM), now known as global system for mobile communication, was considered as a potential method of sending data but was deemed to be unsuitable due to the charges incurred for sending data and the need for network coverage. The system had to be able to communicate in any environment regardless of GSM network availability, and a local wireless network satisfied this requirement.